Overview

Dataset statistics

Number of variables5
Number of observations116
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory43.1 B

Variable types

Categorical3
Numeric2

Dataset

DescriptionSample
Author㈜지오시스템리서치
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT09GSR008

Alerts

SIDO_NM has constant value ""Constant
SGG_NM has constant value ""Constant
TRGET_AREA_NM has constant value ""Constant
VIDO_MNRG_WTCH_YMD has unique valuesUnique
VIDO_MNRG_BCH_XTN has unique valuesUnique

Reproduction

Analysis started2024-03-13 12:53:05.298366
Analysis finished2024-03-13 12:53:06.252519
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

2024-03-13T21:53:06.373014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:53:06.495677image/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

2024-03-13T21:53:06.625955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:53:06.782513image/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

2024-03-13T21:53:06.974056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:53:07.112412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장사 116
100.0%

VIDO_MNRG_WTCH_YMD
Real number (ℝ)

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
2024-03-13T21:53:07.276854image/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
2024-03-13T21:53:07.487996image/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_BCH_XTN
Real number (ℝ)

UNIQUE 

Distinct116
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30788.095
Minimum27008
Maximum33379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-13T21:53:07.692018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27008
5-th percentile27932.75
Q129381.25
median30907
Q332461.75
95-th percentile33150.75
Maximum33379
Range6371
Interquartile range (IQR)3080.5

Descriptive statistics

Standard deviation1769.3729
Coefficient of variation (CV)0.057469386
Kurtosis-1.0229854
Mean30788.095
Median Absolute Deviation (MAD)1555
Skewness-0.33728107
Sum3571419
Variance3130680.4
MonotonicityNot monotonic
2024-03-13T21:53:07.917407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32816 1
 
0.9%
28236 1
 
0.9%
32018 1
 
0.9%
32456 1
 
0.9%
32178 1
 
0.9%
31513 1
 
0.9%
30507 1
 
0.9%
30082 1
 
0.9%
30093 1
 
0.9%
30194 1
 
0.9%
Other values (106) 106
91.4%
ValueCountFrequency (%)
27008 1
0.9%
27059 1
0.9%
27081 1
0.9%
27380 1
0.9%
27661 1
0.9%
27827 1
0.9%
27968 1
0.9%
28100 1
0.9%
28103 1
0.9%
28153 1
0.9%
ValueCountFrequency (%)
33379 1
0.9%
33219 1
0.9%
33172 1
0.9%
33168 1
0.9%
33166 1
0.9%
33159 1
0.9%
33148 1
0.9%
33081 1
0.9%
33063 1
0.9%
33046 1
0.9%

Interactions

2024-03-13T21:53:05.703665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:05.412120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:05.855325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:53:05.560511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:53:08.496951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
VIDO_MNRG_WTCH_YMDVIDO_MNRG_BCH_XTN
VIDO_MNRG_WTCH_YMD1.0000.608
VIDO_MNRG_BCH_XTN0.6081.000
2024-03-13T21:53:08.609187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
VIDO_MNRG_WTCH_YMDVIDO_MNRG_BCH_XTN
VIDO_MNRG_WTCH_YMD1.000-0.091
VIDO_MNRG_BCH_XTN-0.0911.000

Missing values

2024-03-13T21:53:06.033587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:53:06.181851image/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_BCH_XTN
0경상북도영덕군장사2021010132816
1경상북도영덕군장사2021010232899
2경상북도영덕군장사2021010333012
3경상북도영덕군장사2021010433014
4경상북도영덕군장사2021010533063
5경상북도영덕군장사2021010632955
6경상북도영덕군장사2021010732262
7경상북도영덕군장사2021010831552
8경상북도영덕군장사2021010931226
9경상북도영덕군장사2021011030917
SIDO_NMSGG_NMTRGET_AREA_NMVIDO_MNRG_WTCH_YMDVIDO_MNRG_BCH_XTN
106경상북도영덕군장사2021041733219
107경상북도영덕군장사2021041833166
108경상북도영덕군장사2021041933081
109경상북도영덕군장사2021042033379
110경상북도영덕군장사2021042133168
111경상북도영덕군장사2021042232875
112경상북도영덕군장사2021042332539
113경상북도영덕군장사2021042432577
114경상북도영덕군장사2021042532687
115경상북도영덕군장사2021042632398