Overview

Dataset statistics

Number of variables9
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory82.9 B

Variable types

Numeric5
Categorical4

Dataset

Description대구광역시 북구 관내 부동산중개업의 법규위반에 대한 행정처분 결과 분기별 현황 정보(2015년~)를 제공합니다.
URLhttps://www.data.go.kr/data/3037269/fileData.do

Alerts

비고 has constant value ""Constant
데이터기준일자 has constant value ""Constant
적발건수 is highly overall correlated with 계 and 2 other fieldsHigh correlation
계 is highly overall correlated with 적발건수 and 2 other fieldsHigh correlation
업무정지 is highly overall correlated with 적발건수 and 1 other fieldsHigh correlation
과태료 is highly overall correlated with 적발건수 and 1 other fieldsHigh correlation
등록취소 is highly imbalanced (50.3%)Imbalance
적발건수 has 5 (14.7%) zerosZeros
계 has 5 (14.7%) zerosZeros
업무정지 has 9 (26.5%) zerosZeros
과태료 has 11 (32.4%) zerosZeros

Reproduction

Analysis started2023-12-12 06:58:50.923575
Analysis finished2023-12-12 06:58:54.198605
Duration3.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

Distinct9
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.7647
Minimum2015
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T15:58:54.241885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12017
median2019
Q32021
95-th percentile2022.35
Maximum2023
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4991977
Coefficient of variation (CV)0.0012379837
Kurtosis-1.179754
Mean2018.7647
Median Absolute Deviation (MAD)2
Skewness0.036998174
Sum68638
Variance6.2459893
MonotonicityIncreasing
2023-12-12T15:58:54.353262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2015 4
11.8%
2016 4
11.8%
2017 4
11.8%
2018 4
11.8%
2019 4
11.8%
2020 4
11.8%
2021 4
11.8%
2022 4
11.8%
2023 2
5.9%
ValueCountFrequency (%)
2015 4
11.8%
2016 4
11.8%
2017 4
11.8%
2018 4
11.8%
2019 4
11.8%
2020 4
11.8%
2021 4
11.8%
2022 4
11.8%
2023 2
5.9%
ValueCountFrequency (%)
2023 2
5.9%
2022 4
11.8%
2021 4
11.8%
2020 4
11.8%
2019 4
11.8%
2018 4
11.8%
2017 4
11.8%
2016 4
11.8%
2015 4
11.8%

분기
Categorical

Distinct4
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
1
9 
2
9 
3
8 
4
8 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row1

Common Values

ValueCountFrequency (%)
1 9
26.5%
2 9
26.5%
3 8
23.5%
4 8
23.5%

Length

2023-12-12T15:58:54.530932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:58:54.626184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9
26.5%
2 9
26.5%
3 8
23.5%
4 8
23.5%

적발건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0882353
Minimum0
Maximum22
Zeros5
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T15:58:54.827555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6.5
Q310.75
95-th percentile16.35
Maximum22
Range22
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation5.5506901
Coefficient of variation (CV)0.78308491
Kurtosis0.14574274
Mean7.0882353
Median Absolute Deviation (MAD)3.5
Skewness0.70333531
Sum241
Variance30.81016
MonotonicityNot monotonic
2023-12-12T15:58:54.943200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 5
14.7%
8 4
11.8%
3 3
8.8%
14 3
8.8%
6 3
8.8%
7 3
8.8%
5 2
 
5.9%
4 2
 
5.9%
11 2
 
5.9%
1 2
 
5.9%
Other values (5) 5
14.7%
ValueCountFrequency (%)
0 5
14.7%
1 2
 
5.9%
3 3
8.8%
4 2
 
5.9%
5 2
 
5.9%
6 3
8.8%
7 3
8.8%
8 4
11.8%
10 1
 
2.9%
11 2
 
5.9%
ValueCountFrequency (%)
22 1
 
2.9%
17 1
 
2.9%
16 1
 
2.9%
14 3
8.8%
12 1
 
2.9%
11 2
5.9%
10 1
 
2.9%
8 4
11.8%
7 3
8.8%
6 3
8.8%

계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0882353
Minimum0
Maximum22
Zeros5
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T15:58:55.106748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6.5
Q310.75
95-th percentile16.35
Maximum22
Range22
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation5.5506901
Coefficient of variation (CV)0.78308491
Kurtosis0.14574274
Mean7.0882353
Median Absolute Deviation (MAD)3.5
Skewness0.70333531
Sum241
Variance30.81016
MonotonicityNot monotonic
2023-12-12T15:58:55.247418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 5
14.7%
8 4
11.8%
3 3
8.8%
14 3
8.8%
6 3
8.8%
7 3
8.8%
5 2
 
5.9%
4 2
 
5.9%
11 2
 
5.9%
1 2
 
5.9%
Other values (5) 5
14.7%
ValueCountFrequency (%)
0 5
14.7%
1 2
 
5.9%
3 3
8.8%
4 2
 
5.9%
5 2
 
5.9%
6 3
8.8%
7 3
8.8%
8 4
11.8%
10 1
 
2.9%
11 2
 
5.9%
ValueCountFrequency (%)
22 1
 
2.9%
17 1
 
2.9%
16 1
 
2.9%
14 3
8.8%
12 1
 
2.9%
11 2
5.9%
10 1
 
2.9%
8 4
11.8%
7 3
8.8%
6 3
8.8%

등록취소
Categorical

IMBALANCE 

Distinct3
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
0
28 
1
5 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.9%

Sample

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

Common Values

ValueCountFrequency (%)
0 28
82.4%
1 5
 
14.7%
2 1
 
2.9%

Length

2023-12-12T15:58:55.366153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:58:55.476680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
82.4%
1 5
 
14.7%
2 1
 
2.9%

업무정지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8235294
Minimum0
Maximum8
Zeros9
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T15:58:55.601320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median1
Q32
95-th percentile5.7
Maximum8
Range8
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation1.9457894
Coefficient of variation (CV)1.0670458
Kurtosis2.8675651
Mean1.8235294
Median Absolute Deviation (MAD)1
Skewness1.6272643
Sum62
Variance3.7860963
MonotonicityNot monotonic
2023-12-12T15:58:55.741591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 9
26.5%
1 9
26.5%
2 8
23.5%
3 3
 
8.8%
4 2
 
5.9%
5 1
 
2.9%
7 1
 
2.9%
8 1
 
2.9%
ValueCountFrequency (%)
0 9
26.5%
1 9
26.5%
2 8
23.5%
3 3
 
8.8%
4 2
 
5.9%
5 1
 
2.9%
7 1
 
2.9%
8 1
 
2.9%
ValueCountFrequency (%)
8 1
 
2.9%
7 1
 
2.9%
5 1
 
2.9%
4 2
 
5.9%
3 3
 
8.8%
2 8
23.5%
1 9
26.5%
0 9
26.5%

과태료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7647059
Minimum0
Maximum21
Zeros11
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T15:58:55.862404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q36
95-th percentile12.05
Maximum21
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.9730109
Coefficient of variation (CV)1.320956
Kurtosis3.185467
Mean3.7647059
Median Absolute Deviation (MAD)1.5
Skewness1.7400089
Sum128
Variance24.730838
MonotonicityNot monotonic
2023-12-12T15:58:55.979882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 11
32.4%
1 6
17.6%
3 5
14.7%
2 2
 
5.9%
10 2
 
5.9%
6 2
 
5.9%
8 2
 
5.9%
11 1
 
2.9%
21 1
 
2.9%
9 1
 
2.9%
ValueCountFrequency (%)
0 11
32.4%
1 6
17.6%
2 2
 
5.9%
3 5
14.7%
6 2
 
5.9%
8 2
 
5.9%
9 1
 
2.9%
10 2
 
5.9%
11 1
 
2.9%
14 1
 
2.9%
ValueCountFrequency (%)
21 1
 
2.9%
14 1
 
2.9%
11 1
 
2.9%
10 2
 
5.9%
9 1
 
2.9%
8 2
 
5.9%
6 2
 
5.9%
3 5
14.7%
2 2
 
5.9%
1 6
17.6%

비고
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
대구광역시 북구
34 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구광역시 북구
2nd row대구광역시 북구
3rd row대구광역시 북구
4th row대구광역시 북구
5th row대구광역시 북구

Common Values

ValueCountFrequency (%)
대구광역시 북구 34
100.0%

Length

2023-12-12T15:58:56.122595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:58:56.240001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 34
50.0%
북구 34
50.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-06-30
34 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-30
2nd row2023-06-30
3rd row2023-06-30
4th row2023-06-30
5th row2023-06-30

Common Values

ValueCountFrequency (%)
2023-06-30 34
100.0%

Length

2023-12-12T15:58:56.378269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:58:56.474709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-30 34
100.0%

Interactions

2023-12-12T15:58:53.625778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:51.209795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:51.764777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:52.307535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:52.801433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:53.711509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:51.332175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:51.907913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:52.420595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:52.948996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:53.778744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:51.433934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:51.995953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:52.511680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:53.050885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:53.847939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:51.526340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:52.089864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:52.595129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:53.157252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:53.926096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:51.646657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:52.218196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:52.700963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:58:53.543833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:58:56.533044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도분기적발건수계등록취소업무정지과태료
년도1.0000.0000.6260.6260.3520.4820.733
분기0.0001.0000.0000.0000.0000.5840.000
적발건수0.6260.0001.0001.0000.3540.6040.777
계0.6260.0001.0001.0000.3540.6040.777
등록취소0.3520.0000.3540.3541.0000.4710.000
업무정지0.4820.5840.6040.6040.4711.0000.000
과태료0.7330.0000.7770.7770.0000.0001.000
2023-12-12T15:58:56.688826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분기등록취소
분기1.0000.000
등록취소0.0001.000
2023-12-12T15:58:56.818783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도적발건수계업무정지과태료분기등록취소
년도1.0000.3470.347-0.1050.4650.0000.219
적발건수0.3471.0001.0000.5060.7840.0000.000
계0.3471.0001.0000.5060.7840.0000.000
업무정지-0.1050.5060.5061.0000.0350.2610.301
과태료0.4650.7840.7840.0351.0000.0000.000
분기0.0000.0000.0000.2610.0001.0000.000
등록취소0.2190.0000.0000.3010.0000.0001.000

Missing values

2023-12-12T15:58:54.027769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:58:54.155889image/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

년도분기적발건수계등록취소업무정지과태료비고데이터기준일자
02015133003대구광역시 북구2023-06-30
12015255030대구광역시 북구2023-06-30
22015388023대구광역시 북구2023-06-30
3201541414052대구광역시 북구2023-06-30
42016144021대구광역시 북구2023-06-30
52016244021대구광역시 북구2023-06-30
62016366012대구광역시 북구2023-06-30
72016477013대구광역시 북구2023-06-30
82017133021대구광역시 북구2023-06-30
9201721111021대구광역시 북구2023-06-30
년도분기적발건수계등록취소업무정지과태료비고데이터기준일자
242021114140111대구광역시 북구2023-06-30
25202121111036대구광역시 북구2023-06-30
262021388008대구광역시 북구2023-06-30
27202141010118대구광역시 북구2023-06-30
282022188116대구광역시 북구2023-06-30
292022255113대구광역시 북구2023-06-30
302022312120210대구광역시 북구2023-06-30
312022422220121대구광역시 북구2023-06-30
32202311717089대구광역시 북구2023-06-30
332023216160214대구광역시 북구2023-06-30