Overview

Dataset statistics

Number of variables7
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory65.4 B

Variable types

Numeric2
Categorical5

Dataset

Description샘플 데이터
Author국토연구원
URLhttps://bigdata-region.kr/#/dataset/749ae375-6faa-445a-bc70-73e9a5e9c097

Alerts

SPG_NM has constant value ""Constant
SIGNGU_CD has constant value ""Constant
SIGNGU_NM has constant value ""Constant
SPG_SIZE has constant value ""Constant
MDLC_AT has constant value ""Constant
SPG_INNB is highly overall correlated with MDLC_DSTHigh correlation
MDLC_DST is highly overall correlated with SPG_INNBHigh correlation
SPG_INNB has unique valuesUnique
MDLC_DST has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:01:38.983447
Analysis finished2023-12-10 14:01:39.998623
Duration1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SPG_INNB
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.611 × 1015
Minimum3.611 × 1015
Maximum3.611 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:01:40.174039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.611 × 1015
5-th percentile3.611 × 1015
Q13.611 × 1015
median3.611 × 1015
Q33.611 × 1015
95-th percentile3.611 × 1015
Maximum3.611 × 1015
Range7100
Interquartile range (IQR)3700

Descriptive statistics

Standard deviation2203.0308
Coefficient of variation (CV)6.1008885 × 10-13
Kurtosis-1.194984
Mean3.611 × 1015
Median Absolute Deviation (MAD)1950
Skewness0.028979265
Sum1.0833 × 1017
Variance4853344.8
MonotonicityStrictly increasing
2023-12-10T23:01:40.446837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3611000001001100 1
 
3.3%
3611000001005100 1
 
3.3%
3611000001008200 1
 
3.3%
3611000001008100 1
 
3.3%
3611000001007400 1
 
3.3%
3611000001007300 1
 
3.3%
3611000001007200 1
 
3.3%
3611000001007100 1
 
3.3%
3611000001006400 1
 
3.3%
3611000001006300 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3611000001001100 1
3.3%
3611000001001200 1
3.3%
3611000001001300 1
3.3%
3611000001001400 1
3.3%
3611000001002100 1
3.3%
3611000001002200 1
3.3%
3611000001002300 1
3.3%
3611000001002400 1
3.3%
3611000001003100 1
3.3%
3611000001003200 1
3.3%
ValueCountFrequency (%)
3611000001008200 1
3.3%
3611000001008100 1
3.3%
3611000001007400 1
3.3%
3611000001007300 1
3.3%
3611000001007200 1
3.3%
3611000001007100 1
3.3%
3611000001006400 1
3.3%
3611000001006300 1
3.3%
3611000001006200 1
3.3%
3611000001006100 1
3.3%

SPG_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
다바6958
30 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row다바6958
2nd row다바6958
3rd row다바6958
4th row다바6958
5th row다바6958

Common Values

ValueCountFrequency (%)
다바6958 30
100.0%

Length

2023-12-10T23:01:40.664402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:01:40.808514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
다바6958 30
100.0%

SIGNGU_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
36110
30 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36110
2nd row36110
3rd row36110
4th row36110
5th row36110

Common Values

ValueCountFrequency (%)
36110 30
100.0%

Length

2023-12-10T23:01:41.021539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:01:41.209993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
36110 30
100.0%

SIGNGU_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
세종특별자치시
30 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세종특별자치시 30
100.0%

Length

2023-12-10T23:01:41.345064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:01:41.490250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세종특별자치시 30
100.0%

SPG_SIZE
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
50
30 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50
2nd row50
3rd row50
4th row50
5th row50

Common Values

ValueCountFrequency (%)
50 30
100.0%

Length

2023-12-10T23:01:42.001231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:01:42.149131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50 30
100.0%

MDLC_DST
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25105.758
Minimum24864.27
Maximum25354.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:01:42.324286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24864.27
5-th percentile24893.34
Q124989.537
median25105.085
Q325221.077
95-th percentile25319.095
Maximum25354.14
Range489.87
Interquartile range (IQR)231.54

Descriptive statistics

Standard deviation144.68557
Coefficient of variation (CV)0.0057630432
Kurtosis-1.1728085
Mean25105.758
Median Absolute Deviation (MAD)117.045
Skewness0.017099257
Sum753172.75
Variance20933.914
MonotonicityNot monotonic
2023-12-10T23:01:42.511567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25354.14 1
 
3.3%
25091.28 1
 
3.3%
24864.27 1
 
3.3%
24896.53 1
 
3.3%
24890.73 1
 
3.3%
24923.15 1
 
3.3%
24928.84 1
 
3.3%
24961.21 1
 
3.3%
24955.63 1
 
3.3%
24988.17 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
24864.27 1
3.3%
24890.73 1
3.3%
24896.53 1
3.3%
24923.15 1
3.3%
24928.84 1
3.3%
24955.63 1
3.3%
24961.21 1
3.3%
24988.17 1
3.3%
24993.64 1
3.3%
25020.76 1
3.3%
ValueCountFrequency (%)
25354.14 1
3.3%
25321.08 1
3.3%
25316.67 1
3.3%
25288.09 1
3.3%
25283.57 1
3.3%
25255.14 1
3.3%
25250.52 1
3.3%
25222.26 1
3.3%
25217.53 1
3.3%
25189.43 1
3.3%

MDLC_AT
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2
30 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 30
100.0%

Length

2023-12-10T23:01:42.709221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:01:42.848766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 30
100.0%

Interactions

2023-12-10T23:01:39.396860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:39.123649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:39.564310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:39.270139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:01:42.956024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SPG_INNBMDLC_DST
SPG_INNB1.0000.909
MDLC_DST0.9091.000
2023-12-10T23:01:43.075315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SPG_INNBMDLC_DST
SPG_INNB1.000-0.997
MDLC_DST-0.9971.000

Missing values

2023-12-10T23:01:39.747081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:01:39.926392image/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

SPG_INNBSPG_NMSIGNGU_CDSIGNGU_NMSPG_SIZEMDLC_DSTMDLC_AT
03611000001001100다바695836110세종특별자치시5025354.142
13611000001001200다바695836110세종특별자치시5025321.082
23611000001001300다바695836110세종특별자치시5025316.672
33611000001001400다바695836110세종특별자치시5025283.572
43611000001002100다바695836110세종특별자치시5025288.092
53611000001002200다바695836110세종특별자치시5025255.142
63611000001002300다바695836110세종특별자치시5025250.522
73611000001002400다바695836110세종특별자치시5025217.532
83611000001003100다바695836110세종특별자치시5025222.262
93611000001003200다바695836110세종특별자치시5025189.432
SPG_INNBSPG_NMSIGNGU_CDSIGNGU_NMSPG_SIZEMDLC_DSTMDLC_AT
203611000001006100다바695836110세종특별자치시5025026.132
213611000001006200다바695836110세종특별자치시5024993.642
223611000001006300다바695836110세종특별자치시5024988.172
233611000001006400다바695836110세종특별자치시5024955.632
243611000001007100다바695836110세종특별자치시5024961.212
253611000001007200다바695836110세종특별자치시5024928.842
263611000001007300다바695836110세종특별자치시5024923.152
273611000001007400다바695836110세종특별자치시5024890.732
283611000001008100다바695836110세종특별자치시5024896.532
293611000001008200다바695836110세종특별자치시5024864.272