Overview

Dataset statistics

Number of variables6
Number of observations116
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory52.1 B

Variable types

Categorical4
Numeric2

Dataset

DescriptionSample
Author㈜지오시스템리서치
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT09GSR007

Alerts

SIDO_NM has constant value ""Constant
SGG_NM has constant value ""Constant
TRGET_AREA_NM has constant value ""Constant
VIDO_MNRG_BSLN_NO has constant value ""Constant
VIDO_MNRG_WTCH_YMD is highly overall correlated with VIDO_MNRG_BCH_WDTHHigh correlation
VIDO_MNRG_BCH_WDTH is highly overall correlated with VIDO_MNRG_WTCH_YMDHigh correlation
VIDO_MNRG_WTCH_YMD has unique valuesUnique

Reproduction

Analysis started2024-03-13 12:53:09.320846
Analysis finished2024-03-13 12:53:10.269041
Duration0.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SIDO_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
경상북도
116 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 116
100.0%

Length

2024-03-13T21:53:10.359719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:53:10.513384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 116
100.0%

SGG_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
영덕군
116 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영덕군 116
100.0%

Length

2024-03-13T21:53:10.657741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:53:10.766277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영덕군 116
100.0%

TRGET_AREA_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
장사
116 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
장사 116
100.0%

Length

2024-03-13T21:53:10.883500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:53:10.996524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장사 116
100.0%

VIDO_MNRG_WTCH_YMD
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct116
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210260
Minimum20210101
Maximum20210426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-13T21:53:11.117043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210101
5-th percentile20210107
Q120210130
median20210228
Q320210328
95-th percentile20210420
Maximum20210426
Range325
Interquartile range (IQR)198.5

Descriptive statistics

Standard deviation111.07639
Coefficient of variation (CV)5.4960398 × 10-6
Kurtosis-1.3480255
Mean20210260
Median Absolute Deviation (MAD)99.5
Skewness0.028737744
Sum2.3443901 × 109
Variance12337.965
MonotonicityStrictly increasing
2024-03-13T21:53:11.357110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210101 1
 
0.9%
20210316 1
 
0.9%
20210328 1
 
0.9%
20210327 1
 
0.9%
20210326 1
 
0.9%
20210325 1
 
0.9%
20210324 1
 
0.9%
20210323 1
 
0.9%
20210322 1
 
0.9%
20210321 1
 
0.9%
Other values (106) 106
91.4%
ValueCountFrequency (%)
20210101 1
0.9%
20210102 1
0.9%
20210103 1
0.9%
20210104 1
0.9%
20210105 1
0.9%
20210106 1
0.9%
20210107 1
0.9%
20210108 1
0.9%
20210109 1
0.9%
20210110 1
0.9%
ValueCountFrequency (%)
20210426 1
0.9%
20210425 1
0.9%
20210424 1
0.9%
20210423 1
0.9%
20210422 1
0.9%
20210421 1
0.9%
20210420 1
0.9%
20210419 1
0.9%
20210418 1
0.9%
20210417 1
0.9%

VIDO_MNRG_BSLN_NO
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
1
116 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 116
100.0%

Length

2024-03-13T21:53:11.597466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:53:11.740270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 116
100.0%

VIDO_MNRG_BCH_WDTH
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.875862
Minimum35.4
Maximum50.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-13T21:53:11.881055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.4
5-th percentile35.9
Q138.475
median40.4
Q343.3
95-th percentile46.2
Maximum50.4
Range15
Interquartile range (IQR)4.825

Descriptive statistics

Standard deviation3.2591709
Coefficient of variation (CV)0.079733386
Kurtosis-0.10719652
Mean40.875862
Median Absolute Deviation (MAD)2.25
Skewness0.5591488
Sum4741.6
Variance10.622195
MonotonicityNot monotonic
2024-03-13T21:53:12.088392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.8 5
 
4.3%
38.8 4
 
3.4%
37.3 3
 
2.6%
38.5 3
 
2.6%
40.2 3
 
2.6%
41.9 3
 
2.6%
41.0 3
 
2.6%
41.3 3
 
2.6%
39.8 3
 
2.6%
40.4 3
 
2.6%
Other values (64) 83
71.6%
ValueCountFrequency (%)
35.4 1
 
0.9%
35.6 1
 
0.9%
35.7 1
 
0.9%
35.8 2
1.7%
35.9 2
1.7%
36.3 1
 
0.9%
36.4 1
 
0.9%
36.9 2
1.7%
37.1 1
 
0.9%
37.3 3
2.6%
ValueCountFrequency (%)
50.4 1
0.9%
49.8 1
0.9%
48.7 1
0.9%
47.0 1
0.9%
46.9 1
0.9%
46.5 1
0.9%
46.1 1
0.9%
45.9 1
0.9%
45.8 1
0.9%
45.6 1
0.9%

Interactions

2024-03-13T21:53:09.738979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:09.441306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:09.874223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:09.594292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:53:12.207448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
VIDO_MNRG_WTCH_YMDVIDO_MNRG_BCH_WDTH
VIDO_MNRG_WTCH_YMD1.0000.741
VIDO_MNRG_BCH_WDTH0.7411.000
2024-03-13T21:53:12.346153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
VIDO_MNRG_WTCH_YMDVIDO_MNRG_BCH_WDTH
VIDO_MNRG_WTCH_YMD1.0000.575
VIDO_MNRG_BCH_WDTH0.5751.000

Missing values

2024-03-13T21:53:10.044290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:53:10.201144image/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

SIDO_NMSGG_NMTRGET_AREA_NMVIDO_MNRG_WTCH_YMDVIDO_MNRG_BSLN_NOVIDO_MNRG_BCH_WDTH
0경상북도영덕군장사20210101136.9
1경상북도영덕군장사20210102137.8
2경상북도영덕군장사20210103139.0
3경상북도영덕군장사20210104140.1
4경상북도영덕군장사20210105140.8
5경상북도영덕군장사20210106140.6
6경상북도영덕군장사20210107139.7
7경상북도영덕군장사20210108138.8
8경상북도영덕군장사20210109137.8
9경상북도영덕군장사20210110138.1
SIDO_NMSGG_NMTRGET_AREA_NMVIDO_MNRG_WTCH_YMDVIDO_MNRG_BSLN_NOVIDO_MNRG_BCH_WDTH
106경상북도영덕군장사20210417144.7
107경상북도영덕군장사20210418144.2
108경상북도영덕군장사20210419143.3
109경상북도영덕군장사20210420143.6
110경상북도영덕군장사20210421142.3
111경상북도영덕군장사20210422140.7
112경상북도영덕군장사20210423139.0
113경상북도영덕군장사20210424140.2
114경상북도영덕군장사20210425141.9
115경상북도영덕군장사20210426141.3