Overview

Dataset statistics

Number of variables7
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory65.8 B

Variable types

Text1
Numeric6

Dataset

Description전북특별자치도 건설업 등록 현황(변동사항-증감재, 등록, 전입, 전출, 등록말소, 2019년 12월까지 누계 등) 데이터입니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/3081433/fileData.do

Alerts

변동사항(증감재) is highly overall correlated with 변동사항(등록) and 4 other fieldsHigh correlation
변동사항(등록) is highly overall correlated with 변동사항(증감재) and 4 other fieldsHigh correlation
변동사항(전입) is highly overall correlated with 변동사항(증감재) and 4 other fieldsHigh correlation
변동사항(전출) is highly overall correlated with 변동사항(증감재) and 4 other fieldsHigh correlation
변동사항(등록말소) is highly overall correlated with 변동사항(증감재) and 4 other fieldsHigh correlation
2019년 12월까지 누계 is highly overall correlated with 변동사항(증감재) and 4 other fieldsHigh correlation
구분 has unique valuesUnique
변동사항(증감재) has 7 (20.6%) zerosZeros
변동사항(등록) has 5 (14.7%) zerosZeros
변동사항(전입) has 8 (23.5%) zerosZeros
변동사항(전출) has 11 (32.4%) zerosZeros
변동사항(등록말소) has 5 (14.7%) zerosZeros
2019년 12월까지 누계 has 1 (2.9%) zerosZeros

Reproduction

Analysis started2024-03-14 09:57:14.279030
Analysis finished2024-03-14 09:57:22.349322
Duration8.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size400.0 B
2024-03-14T18:57:22.988024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length8
Min length4

Characters and Unicode

Total characters272
Distinct characters74
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)100.0%

Sample

1st row토목건축공사업
2nd row토목공사업
3rd row건축공사업
4th row조경공사업
5th row산업ㆍ환경설비공사업
ValueCountFrequency (%)
가스시설시공업 3
 
7.5%
난방시공업 3
 
7.5%
제1종 2
 
5.0%
제3종 2
 
5.0%
제2종 2
 
5.0%
금속구조물ㆍ창호ㆍ온실공사업 1
 
2.5%
습식ㆍ방수공사업 1
 
2.5%
시설물유지관리업 1
 
2.5%
토목건축공사업 1
 
2.5%
조경식재공사업 1
 
2.5%
Other values (23) 23
57.5%
2024-03-14T18:57:24.186065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 34
 
12.5%
공 33
 
12.1%
사 27
 
9.9%
설 13
 
4.8%
시 11
 
4.0%
조 7
 
2.6%
6
 
2.2%
제 6
 
2.2%
종 6
 
2.2%
물 6
 
2.2%
Other values (64) 123
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 255
93.8%
Space Separator 6
 
2.2%
Decimal Number 6
 
2.2%
Other Punctuation 5
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 34
 
13.3%
공 33
 
12.9%
사 27
 
10.6%
설 13
 
5.1%
시 11
 
4.3%
조 7
 
2.7%
제 6
 
2.4%
종 6
 
2.4%
물 6
 
2.4%
ㆍ 5
 
2.0%
Other values (59) 107
42.0%
Decimal Number
ValueCountFrequency (%)
1 2
33.3%
3 2
33.3%
2 2
33.3%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
· 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 255
93.8%
Common 17
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 34
 
13.3%
공 33
 
12.9%
사 27
 
10.6%
설 13
 
5.1%
시 11
 
4.3%
조 7
 
2.7%
제 6
 
2.4%
종 6
 
2.4%
물 6
 
2.4%
ㆍ 5
 
2.0%
Other values (59) 107
42.0%
Common
ValueCountFrequency (%)
6
35.3%
· 5
29.4%
1 2
 
11.8%
3 2
 
11.8%
2 2
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 250
91.9%
ASCII 12
 
4.4%
None 5
 
1.8%
Compat Jamo 5
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 34
 
13.6%
공 33
 
13.2%
사 27
 
10.8%
설 13
 
5.2%
시 11
 
4.4%
조 7
 
2.8%
제 6
 
2.4%
종 6
 
2.4%
물 6
 
2.4%
도 5
 
2.0%
Other values (58) 102
40.8%
ASCII
ValueCountFrequency (%)
6
50.0%
1 2
 
16.7%
3 2
 
16.7%
2 2
 
16.7%
None
ValueCountFrequency (%)
· 5
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 5
100.0%

변동사항(증감재)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.058824
Minimum-5
Maximum73
Zeros7
Zeros (%)20.6%
Negative2
Negative (%)5.9%
Memory size434.0 B
2024-03-14T18:57:24.556496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile-0.35
Q10.25
median5.5
Q316
95-th percentile33.5
Maximum73
Range78
Interquartile range (IQR)15.75

Descriptive statistics

Standard deviation15.107576
Coefficient of variation (CV)1.3661106
Kurtosis7.7764589
Mean11.058824
Median Absolute Deviation (MAD)5.5
Skewness2.3853096
Sum376
Variance228.23886
MonotonicityNot monotonic
2024-03-14T18:57:24.950322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 7
20.6%
2 3
 
8.8%
24 2
 
5.9%
14 2
 
5.9%
16 2
 
5.9%
5 2
 
5.9%
-5 1
 
2.9%
3 1
 
2.9%
30 1
 
2.9%
15 1
 
2.9%
Other values (12) 12
35.3%
ValueCountFrequency (%)
-5 1
 
2.9%
-1 1
 
2.9%
0 7
20.6%
1 1
 
2.9%
2 3
8.8%
3 1
 
2.9%
4 1
 
2.9%
5 2
 
5.9%
6 1
 
2.9%
7 1
 
2.9%
ValueCountFrequency (%)
73 1
2.9%
40 1
2.9%
30 1
2.9%
24 2
5.9%
22 1
2.9%
20 1
2.9%
17 1
2.9%
16 2
5.9%
15 1
2.9%
14 2
5.9%

변동사항(등록)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.852941
Minimum0
Maximum96
Zeros5
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size434.0 B
2024-03-14T18:57:25.341412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.25
median10
Q323.5
95-th percentile42.7
Maximum96
Range96
Interquartile range (IQR)20.25

Descriptive statistics

Standard deviation18.977071
Coefficient of variation (CV)1.1970694
Kurtosis8.7879585
Mean15.852941
Median Absolute Deviation (MAD)9.5
Skewness2.4735553
Sum539
Variance360.12923
MonotonicityNot monotonic
2024-03-14T18:57:25.730283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 5
 
14.7%
4 4
 
11.8%
5 2
 
5.9%
1 2
 
5.9%
42 1
 
2.9%
44 1
 
2.9%
17 1
 
2.9%
29 1
 
2.9%
14 1
 
2.9%
8 1
 
2.9%
Other values (15) 15
44.1%
ValueCountFrequency (%)
0 5
14.7%
1 2
 
5.9%
2 1
 
2.9%
3 1
 
2.9%
4 4
11.8%
5 2
 
5.9%
7 1
 
2.9%
8 1
 
2.9%
12 1
 
2.9%
14 1
 
2.9%
ValueCountFrequency (%)
96 1
2.9%
44 1
2.9%
42 1
2.9%
31 1
2.9%
30 1
2.9%
29 1
2.9%
27 1
2.9%
25 1
2.9%
24 1
2.9%
22 1
2.9%

변동사항(전입)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.941176
Minimum0
Maximum62
Zeros8
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size434.0 B
2024-03-14T18:57:26.076225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3.5
Q316.25
95-th percentile31
Maximum62
Range62
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation14.058193
Coefficient of variation (CV)1.2848886
Kurtosis3.8729036
Mean10.941176
Median Absolute Deviation (MAD)3.5
Skewness1.7957384
Sum372
Variance197.6328
MonotonicityNot monotonic
2024-03-14T18:57:26.450814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 8
23.5%
3 4
11.8%
31 3
 
8.8%
2 3
 
8.8%
1 2
 
5.9%
10 2
 
5.9%
12 2
 
5.9%
4 2
 
5.9%
22 1
 
2.9%
30 1
 
2.9%
Other values (6) 6
17.6%
ValueCountFrequency (%)
0 8
23.5%
1 2
 
5.9%
2 3
 
8.8%
3 4
11.8%
4 2
 
5.9%
10 2
 
5.9%
11 1
 
2.9%
12 2
 
5.9%
14 1
 
2.9%
17 1
 
2.9%
ValueCountFrequency (%)
62 1
 
2.9%
31 3
8.8%
30 1
 
2.9%
27 1
 
2.9%
24 1
 
2.9%
22 1
 
2.9%
17 1
 
2.9%
14 1
 
2.9%
12 2
5.9%
11 1
 
2.9%

변동사항(전출)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9411765
Minimum0
Maximum54
Zeros11
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size434.0 B
2024-03-14T18:57:26.817525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q316.25
95-th percentile31.35
Maximum54
Range54
Interquartile range (IQR)16.25

Descriptive statistics

Standard deviation13.137827
Coefficient of variation (CV)1.3215566
Kurtosis2.4793767
Mean9.9411765
Median Absolute Deviation (MAD)5
Skewness1.6051925
Sum338
Variance172.6025
MonotonicityNot monotonic
2024-03-14T18:57:27.202934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 11
32.4%
1 4
 
11.8%
9 3
 
8.8%
6 2
 
5.9%
30 2
 
5.9%
10 1
 
2.9%
20 1
 
2.9%
4 1
 
2.9%
32 1
 
2.9%
3 1
 
2.9%
Other values (7) 7
20.6%
ValueCountFrequency (%)
0 11
32.4%
1 4
 
11.8%
3 1
 
2.9%
4 1
 
2.9%
6 2
 
5.9%
7 1
 
2.9%
9 3
 
8.8%
10 1
 
2.9%
11 1
 
2.9%
18 1
 
2.9%
ValueCountFrequency (%)
54 1
2.9%
32 1
2.9%
31 1
2.9%
30 2
5.9%
24 1
2.9%
21 1
2.9%
20 1
2.9%
18 1
2.9%
11 1
2.9%
10 1
2.9%

변동사항(등록말소)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7941176
Minimum0
Maximum31
Zeros5
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size434.0 B
2024-03-14T18:57:27.546999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median3
Q37.75
95-th percentile18.1
Maximum31
Range31
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation7.0098606
Coefficient of variation (CV)1.2098237
Kurtosis4.3755922
Mean5.7941176
Median Absolute Deviation (MAD)2.5
Skewness1.9959178
Sum197
Variance49.138146
MonotonicityNot monotonic
2024-03-14T18:57:27.907704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 7
20.6%
0 5
14.7%
1 4
11.8%
4 3
8.8%
6 2
 
5.9%
16 2
 
5.9%
3 2
 
5.9%
14 1
 
2.9%
9 1
 
2.9%
5 1
 
2.9%
Other values (6) 6
17.6%
ValueCountFrequency (%)
0 5
14.7%
1 4
11.8%
2 7
20.6%
3 2
 
5.9%
4 3
8.8%
5 1
 
2.9%
6 2
 
5.9%
7 1
 
2.9%
8 1
 
2.9%
9 1
 
2.9%
ValueCountFrequency (%)
31 1
2.9%
22 1
2.9%
16 2
5.9%
14 1
2.9%
11 1
2.9%
10 1
2.9%
9 1
2.9%
8 1
2.9%
7 1
2.9%
6 2
5.9%

2019년 12월까지 누계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.26471
Minimum0
Maximum1070
Zeros1
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size434.0 B
2024-03-14T18:57:28.306819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.65
Q124
median151.5
Q3286.25
95-th percentile458.25
Maximum1070
Range1070
Interquartile range (IQR)262.25

Descriptive statistics

Standard deviation210.7101
Coefficient of variation (CV)1.1074576
Kurtosis8.2762322
Mean190.26471
Median Absolute Deviation (MAD)129
Skewness2.3477439
Sum6469
Variance44398.746
MonotonicityNot monotonic
2024-03-14T18:57:28.711485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2 2
 
5.9%
24 2
 
5.9%
244 1
 
2.9%
310 1
 
2.9%
439 1
 
2.9%
106 1
 
2.9%
423 1
 
2.9%
3 1
 
2.9%
294 1
 
2.9%
65 1
 
2.9%
Other values (22) 22
64.7%
ValueCountFrequency (%)
0 1
2.9%
1 1
2.9%
2 2
5.9%
3 1
2.9%
12 1
2.9%
14 1
2.9%
21 1
2.9%
24 2
5.9%
44 1
2.9%
65 1
2.9%
ValueCountFrequency (%)
1070 1
2.9%
494 1
2.9%
439 1
2.9%
423 1
2.9%
365 1
2.9%
310 1
2.9%
305 1
2.9%
295 1
2.9%
294 1
2.9%
263 1
2.9%

Interactions

2024-03-14T18:57:20.545691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:14.531283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:16.035658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:17.497143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:18.635578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:19.578964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:20.699953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:14.788278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:16.286802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:17.918298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:18.872038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:19.737281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:20.837683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:15.034802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:16.523866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:18.059191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:19.012259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:19.888982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:21.022642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:15.287766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:16.770121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:18.206532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:19.159177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:20.112241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:21.246737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:15.535828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:17.008849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:18.346024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:19.294961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:20.256904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:21.460010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:15.787989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:17.257942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:18.494470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:19.440257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:57:20.404531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:57:28.965629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분변동사항(증감재)변동사항(등록)변동사항(전입)변동사항(전출)변동사항(등록말소)2019년 12월까지 누계
구분1.0001.0001.0001.0001.0001.0001.000
변동사항(증감재)1.0001.0000.8490.9340.9470.7510.730
변동사항(등록)1.0000.8491.0000.7280.7730.7630.904
변동사항(전입)1.0000.9340.7281.0000.9720.8160.859
변동사항(전출)1.0000.9470.7730.9721.0000.7650.783
변동사항(등록말소)1.0000.7510.7630.8160.7651.0000.878
2019년 12월까지 누계1.0000.7300.9040.8590.7830.8781.000
2024-03-14T18:57:29.261095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
변동사항(증감재)변동사항(등록)변동사항(전입)변동사항(전출)변동사항(등록말소)2019년 12월까지 누계
변동사항(증감재)1.0000.9150.7960.7200.5820.756
변동사항(등록)0.9151.0000.8090.7580.7890.899
변동사항(전입)0.7960.8091.0000.9230.7020.827
변동사항(전출)0.7200.7580.9231.0000.5570.734
변동사항(등록말소)0.5820.7890.7020.5571.0000.873
2019년 12월까지 누계0.7560.8990.8270.7340.8731.000

Missing values

2024-03-14T18:57:21.795296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:57:22.191506image/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

구분변동사항(증감재)변동사항(등록)변동사항(전입)변동사항(전출)변동사항(등록말소)2019년 12월까지 누계
0토목건축공사업-54366244
1토목공사업712463234
2건축공사업243014911295
3조경공사업25306107
4산업ㆍ환경설비공사업0010121
5실내건축공사업22251094169
6토공사업827272422365
7석공사업14191298176
8도장공사업162412182193
9비계·구조물해체공사121831307182
구분변동사항(증감재)변동사항(등록)변동사항(전입)변동사항(전출)변동사항(등록말소)2019년 12월까지 누계
24가스시설시공업 제1종-1223244
25가스시설시공업 제2종1421209305
26가스시설시공업 제3종68314134
27난방시공업 제1종5431165
28난방시공업 제2종2142014294
29난방시공업 제3종000003
30시설물유지관리업242930323423
31습식ㆍ방수공사업1517442106
32금속구조물ㆍ창호ㆍ온실공사업3044222016439
33지붕판금ㆍ건축물조립공사업2401124