Overview

Dataset statistics

Number of variables10
Number of observations3225
Missing cells3225
Missing cells (%)10.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory261.5 KiB
Average record size in memory83.0 B

Variable types

Text2
Categorical5
Unsupported1
Numeric1
DateTime1

Dataset

Description충청남도 예산군_충청남도 예산군_도시계획정보시스템 개발행위허가필지도 데이터 베이스로 개발행위허가지역에 관련하여 도면, 면적, 시군 구 코드등이 담겨있음.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=12&beforeMenuCd=DOM_000000201001001000&publicdatapk=15123962

Alerts

LCLAS_CL has constant value ""Constant
SIGNGU_SE has constant value ""Constant
CREATE_DAT has constant value ""Constant
MLSFC_CL is highly overall correlated with ATRB_SE and 1 other fieldsHigh correlation
ATRB_SE is highly overall correlated with MLSFC_CL and 1 other fieldsHigh correlation
DGM_NM is highly overall correlated with MLSFC_CL and 1 other fieldsHigh correlation
SCLAS_CL has 3225 (100.0%) missing valuesMissing
DGM_AR is highly skewed (γ1 = 26.47834231)Skewed
PRESENT_SN has unique valuesUnique
SCLAS_CL is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-01-09 21:51:36.231042
Analysis finished2024-01-09 21:51:37.038455
Duration0.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

PRESENT_SN
Text

UNIQUE 

Distinct3225
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
2024-01-10T06:51:37.154479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters77400
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3225 ?
Unique (%)100.0%

Sample

1st row44810UQ174PS201004301796
2nd row44810UQ174PS201004301506
3rd row44810UQ174PS201101013264
4th row44810UQ174PS201004302548
5th row44810UQ174PS201301013669
ValueCountFrequency (%)
44810uq174ps201004301796 1
 
< 0.1%
44810uq174ps201004301605 1
 
< 0.1%
44810uq174ps201004301414 1
 
< 0.1%
44810uq174ps201004300484 1
 
< 0.1%
44810uq174ps201101013085 1
 
< 0.1%
44810uq174ps201004301716 1
 
< 0.1%
44810uq174ps201004300704 1
 
< 0.1%
44810uq174ps201004300752 1
 
< 0.1%
44810uq174ps201004300461 1
 
< 0.1%
44810uq174ps201004301427 1
 
< 0.1%
Other values (3215) 3215
99.7%
2024-01-10T06:51:37.415521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16928
21.9%
1 13693
17.7%
4 13284
17.2%
2 5105
 
6.6%
3 4233
 
5.5%
7 4194
 
5.4%
8 4163
 
5.4%
U 3225
 
4.2%
Q 3225
 
4.2%
P 3225
 
4.2%
Other values (4) 6125
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 64500
83.3%
Uppercase Letter 12900
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16928
26.2%
1 13693
21.2%
4 13284
20.6%
2 5105
 
7.9%
3 4233
 
6.6%
7 4194
 
6.5%
8 4163
 
6.5%
9 974
 
1.5%
5 969
 
1.5%
6 957
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
U 3225
25.0%
Q 3225
25.0%
P 3225
25.0%
S 3225
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 64500
83.3%
Latin 12900
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16928
26.2%
1 13693
21.2%
4 13284
20.6%
2 5105
 
7.9%
3 4233
 
6.6%
7 4194
 
6.5%
8 4163
 
6.5%
9 974
 
1.5%
5 969
 
1.5%
6 957
 
1.5%
Latin
ValueCountFrequency (%)
U 3225
25.0%
Q 3225
25.0%
P 3225
25.0%
S 3225
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16928
21.9%
1 13693
17.7%
4 13284
17.2%
2 5105
 
6.6%
3 4233
 
5.5%
7 4194
 
5.4%
8 4163
 
5.4%
U 3225
 
4.2%
Q 3225
 
4.2%
P 3225
 
4.2%
Other values (4) 6125
 
7.9%

LCLAS_CL
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
UQQA00
3225 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUQQA00
2nd rowUQQA00
3rd rowUQQA00
4th rowUQQA00
5th rowUQQA00

Common Values

ValueCountFrequency (%)
UQQA00 3225
100.0%

Length

2024-01-10T06:51:37.521375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:51:37.589295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
uqqa00 3225
100.0%

MLSFC_CL
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
UQQA20
2273 
UQQA40
664 
UQQA30
 
181
UQQA10
 
65
UQQA50
 
42

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUQQA20
2nd rowUQQA20
3rd rowUQQA40
4th rowUQQA20
5th rowUQQA20

Common Values

ValueCountFrequency (%)
UQQA20 2273
70.5%
UQQA40 664
 
20.6%
UQQA30 181
 
5.6%
UQQA10 65
 
2.0%
UQQA50 42
 
1.3%

Length

2024-01-10T06:51:37.677091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:51:37.772141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
uqqa20 2273
70.5%
uqqa40 664
 
20.6%
uqqa30 181
 
5.6%
uqqa10 65
 
2.0%
uqqa50 42
 
1.3%

SCLAS_CL
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3225
Missing (%)100.0%
Memory size28.5 KiB

ATRB_SE
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
UQQA20
2273 
UQQA40
664 
UQQA30
 
181
UQQA10
 
65
UQQA50
 
42

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUQQA20
2nd rowUQQA20
3rd rowUQQA40
4th rowUQQA20
5th rowUQQA20

Common Values

ValueCountFrequency (%)
UQQA20 2273
70.5%
UQQA40 664
 
20.6%
UQQA30 181
 
5.6%
UQQA10 65
 
2.0%
UQQA50 42
 
1.3%

Length

2024-01-10T06:51:37.862359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:51:37.943241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
uqqa20 2273
70.5%
uqqa40 664
 
20.6%
uqqa30 181
 
5.6%
uqqa10 65
 
2.0%
uqqa50 42
 
1.3%

PERM_SE
Text

Distinct2308
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
2024-01-10T06:51:38.121497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters64500
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1799 ?
Unique (%)55.8%

Sample

1st row44810PPR201004301783
2nd row44810PPR201004301567
3rd row44810PPR201101012044
4th row44810PPR201004304012
5th row44810PPR201301012305
ValueCountFrequency (%)
44810ppr201004302801 24
 
0.7%
44810ppr201004301139 19
 
0.6%
44810ppr201004301702 11
 
0.3%
44810ppr201201012274 11
 
0.3%
44810ppr201004301585 11
 
0.3%
44810ppr201004301479 10
 
0.3%
44810ppr201301012352 10
 
0.3%
44810ppr201004301375 10
 
0.3%
44810ppr201004301277 9
 
0.3%
44810ppr201004301931 8
 
0.2%
Other values (2298) 3102
96.2%
2024-01-10T06:51:38.417396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16793
26.0%
1 10491
16.3%
4 10030
15.6%
P 6450
 
10.0%
2 6063
 
9.4%
8 4049
 
6.3%
3 3540
 
5.5%
R 3225
 
5.0%
9 1024
 
1.6%
5 1010
 
1.6%
Other values (2) 1825
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54825
85.0%
Uppercase Letter 9675
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16793
30.6%
1 10491
19.1%
4 10030
18.3%
2 6063
 
11.1%
8 4049
 
7.4%
3 3540
 
6.5%
9 1024
 
1.9%
5 1010
 
1.8%
6 913
 
1.7%
7 912
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
P 6450
66.7%
R 3225
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 54825
85.0%
Latin 9675
 
15.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16793
30.6%
1 10491
19.1%
4 10030
18.3%
2 6063
 
11.1%
8 4049
 
7.4%
3 3540
 
6.5%
9 1024
 
1.9%
5 1010
 
1.8%
6 913
 
1.7%
7 912
 
1.7%
Latin
ValueCountFrequency (%)
P 6450
66.7%
R 3225
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16793
26.0%
1 10491
16.3%
4 10030
15.6%
P 6450
 
10.0%
2 6063
 
9.4%
8 4049
 
6.3%
3 3540
 
5.5%
R 3225
 
5.0%
9 1024
 
1.6%
5 1010
 
1.6%
Other values (2) 1825
 
2.8%

DGM_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
토지형질변경
2273 
토지분할
664 
토석채취
 
181
공작물설치
 
65
물건적치
 
42

Length

Max length6
Median length6
Mean length5.4297674
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row토지형질변경
2nd row토지형질변경
3rd row토지분할
4th row토지형질변경
5th row토지형질변경

Common Values

ValueCountFrequency (%)
토지형질변경 2273
70.5%
토지분할 664
 
20.6%
토석채취 181
 
5.6%
공작물설치 65
 
2.0%
물건적치 42
 
1.3%

Length

2024-01-10T06:51:38.539742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:51:38.626824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
토지형질변경 2273
70.5%
토지분할 664
 
20.6%
토석채취 181
 
5.6%
공작물설치 65
 
2.0%
물건적치 42
 
1.3%

DGM_AR
Real number (ℝ)

SKEWED 

Distinct3213
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3745.5149
Minimum0.18
Maximum1063463.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2024-01-10T06:51:38.723552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.18
5-th percentile129.604
Q1435.55
median881.3
Q32008.03
95-th percentile8275.514
Maximum1063463.9
Range1063463.7
Interquartile range (IQR)1572.48

Descriptive statistics

Standard deviation31190.576
Coefficient of variation (CV)8.3274467
Kurtosis814.5134
Mean3745.5149
Median Absolute Deviation (MAD)563.08
Skewness26.478342
Sum12079286
Variance9.7285203 × 108
MonotonicityNot monotonic
2024-01-10T06:51:38.831894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
504.71 3
 
0.1%
835.31 2
 
0.1%
373.63 2
 
0.1%
542.35 2
 
0.1%
408.65 2
 
0.1%
636.71 2
 
0.1%
569.52 2
 
0.1%
725.1 2
 
0.1%
659.86 2
 
0.1%
437.25 2
 
0.1%
Other values (3203) 3204
99.3%
ValueCountFrequency (%)
0.18 1
< 0.1%
2.34 1
< 0.1%
3.65 1
< 0.1%
5.59 1
< 0.1%
5.95 1
< 0.1%
7.26 1
< 0.1%
7.48 1
< 0.1%
10.02 1
< 0.1%
11.04 1
< 0.1%
11.93 1
< 0.1%
ValueCountFrequency (%)
1063463.88 1
< 0.1%
1023527.48 1
< 0.1%
550428.51 1
< 0.1%
435297.66 1
< 0.1%
297905.03 1
< 0.1%
282455.12 1
< 0.1%
255842.02 1
< 0.1%
254479.01 1
< 0.1%
152276.79 1
< 0.1%
147756.68 1
< 0.1%

SIGNGU_SE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
44810
3225 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44810
2nd row44810
3rd row44810
4th row44810
5th row44810

Common Values

ValueCountFrequency (%)
44810 3225
100.0%

Length

2024-01-10T06:51:38.938684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:51:39.015122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44810 3225
100.0%

CREATE_DAT
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
Minimum2015-01-31 00:00:00
Maximum2015-01-31 00:00:00
2024-01-10T06:51:39.071066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:39.139897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-10T06:51:36.557523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:51:39.192724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MLSFC_CLATRB_SEDGM_NMDGM_AR
MLSFC_CL1.0001.0001.0000.052
ATRB_SE1.0001.0001.0000.052
DGM_NM1.0001.0001.0000.052
DGM_AR0.0520.0520.0521.000
2024-01-10T06:51:39.264085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MLSFC_CLATRB_SEDGM_NM
MLSFC_CL1.0001.0001.000
ATRB_SE1.0001.0001.000
DGM_NM1.0001.0001.000
2024-01-10T06:51:39.331208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DGM_ARMLSFC_CLATRB_SEDGM_NM
DGM_AR1.0000.0350.0350.035
MLSFC_CL0.0351.0001.0001.000
ATRB_SE0.0351.0001.0001.000
DGM_NM0.0351.0001.0001.000

Missing values

2024-01-10T06:51:36.656317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:51:36.994125image/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

PRESENT_SNLCLAS_CLMLSFC_CLSCLAS_CLATRB_SEPERM_SEDGM_NMDGM_ARSIGNGU_SECREATE_DAT
044810UQ174PS201004301796UQQA00UQQA20<NA>UQQA2044810PPR201004301783토지형질변경1348.34448102015-01-31
144810UQ174PS201004301506UQQA00UQQA20<NA>UQQA2044810PPR201004301567토지형질변경3653.39448102015-01-31
244810UQ174PS201101013264UQQA00UQQA40<NA>UQQA4044810PPR201101012044토지분할911.52448102015-01-31
344810UQ174PS201004302548UQQA00UQQA20<NA>UQQA2044810PPR201004304012토지형질변경537.62448102015-01-31
444810UQ174PS201301013669UQQA00UQQA20<NA>UQQA2044810PPR201301012305토지형질변경2159.4448102015-01-31
544810UQ174PS201301013670UQQA00UQQA20<NA>UQQA2044810PPR201301012305토지형질변경2214.76448102015-01-31
644810UQ174PS201004302607UQQA00UQQA20<NA>UQQA2044810PPR201004302679토지형질변경114.66448102015-01-31
744810UQ174PS201101013268UQQA00UQQA40<NA>UQQA4044810PPR201101012046토지분할255.14448102015-01-31
844810UQ174PS201101013257UQQA00UQQA40<NA>UQQA4044810PPR201101012040토지분할1254.7448102015-01-31
944810UQ174PS201004300193UQQA00UQQA20<NA>UQQA2044810PPR201004301535토지형질변경3096.18448102015-01-31
PRESENT_SNLCLAS_CLMLSFC_CLSCLAS_CLATRB_SEPERM_SEDGM_NMDGM_ARSIGNGU_SECREATE_DAT
321544810UQ174PS201004303044UQQA00UQQA20<NA>UQQA2044810PPR201004302051토지형질변경651.6448102015-01-31
321644810UQ174PS201004302667UQQA00UQQA20<NA>UQQA2044810PPR201004302739토지형질변경130.68448102015-01-31
321744810UQ174PS201004300165UQQA00UQQA10<NA>UQQA1044810PPR201004301507공작물설치355.76448102015-01-31
321844810UQ174PS201201013406UQQA00UQQA40<NA>UQQA4044810PPR201201012145토지분할2428.71448102015-01-31
321944810UQ174PS201004302119UQQA00UQQA20<NA>UQQA2044810PPR201004302176토지형질변경1316.9448102015-01-31
322044810UQ174PS201004301125UQQA00UQQA20<NA>UQQA2044810PPR201004301185토지형질변경578.51448102015-01-31
322144810UQ174PS201004302490UQQA00UQQA40<NA>UQQA4044810PPR201004303059토지분할421.41448102015-01-31
322244810UQ174PS201004301996UQQA00UQQA20<NA>UQQA2044810PPR201004300983토지형질변경210.32448102015-01-31
322344810UQ174PS201004303030UQQA00UQQA40<NA>UQQA4044810PPR201004303030토지분할630.35448102015-01-31
322444810UQ174PS201004302058UQQA00UQQA50<NA>UQQA5044810PPR201004302117물건적치2805.13448102015-01-31