Overview

Dataset statistics

Number of variables10
Number of observations199
Missing cells398
Missing cells (%)20.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.8 KiB
Average record size in memory86.7 B

Variable types

Categorical3
Numeric3
Unsupported2
DateTime2

Dataset

DescriptionSample
Author소상공인연합회
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KFMSTR02201911010001

Alerts

201501 has constant value ""Constant
30-Sep-2019 has constant value ""Constant
AA3 has constant value ""Constant
행정동추정임대료 has constant value ""Constant
10001567 is highly overall correlated with 10001567.1High correlation
10001567.1 is highly overall correlated with 10001567High correlation
Unnamed: 3 has 199 (100.0%) missing valuesMissing
Unnamed: 5 has 199 (100.0%) missing valuesMissing
10001567 has unique valuesUnique
10001567.1 has unique valuesUnique
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 06:46:24.361122
Analysis finished2023-12-10 06:46:25.910295
Duration1.55 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

201501
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
201501
199 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201501
2nd row201501
3rd row201501
4th row201501
5th row201501

Common Values

ValueCountFrequency (%)
201501 199
100.0%

Length

2023-12-10T15:46:25.998023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:26.139656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201501 199
100.0%

10001567
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10318854
Minimum10003682
Maximum10605375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:46:26.274976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10003682
5-th percentile10046814
Q110180741
median10330315
Q310463110
95-th percentile10563732
Maximum10605375
Range601693
Interquartile range (IQR)282368.5

Descriptive statistics

Standard deviation165901.45
Coefficient of variation (CV)0.016077506
Kurtosis-1.0934127
Mean10318854
Median Absolute Deviation (MAD)142677
Skewness-0.096123579
Sum2.053452 × 109
Variance2.7523291 × 1010
MonotonicityStrictly increasing
2023-12-10T15:46:26.430033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10003682 1
 
0.5%
10431700 1
 
0.5%
10395373 1
 
0.5%
10395773 1
 
0.5%
10397990 1
 
0.5%
10398986 1
 
0.5%
10399886 1
 
0.5%
10402553 1
 
0.5%
10403730 1
 
0.5%
10406144 1
 
0.5%
Other values (189) 189
95.0%
ValueCountFrequency (%)
10003682 1
0.5%
10003921 1
0.5%
10009016 1
0.5%
10013115 1
0.5%
10021471 1
0.5%
10021493 1
0.5%
10023016 1
0.5%
10025748 1
0.5%
10028807 1
0.5%
10044560 1
0.5%
ValueCountFrequency (%)
10605375 1
0.5%
10604231 1
0.5%
10603645 1
0.5%
10602454 1
0.5%
10586213 1
0.5%
10581318 1
0.5%
10574521 1
0.5%
10572265 1
0.5%
10567471 1
0.5%
10565923 1
0.5%

10001567.1
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10318854
Minimum10003682
Maximum10605375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:46:26.847576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10003682
5-th percentile10046814
Q110180741
median10330315
Q310463110
95-th percentile10563732
Maximum10605375
Range601693
Interquartile range (IQR)282368.5

Descriptive statistics

Standard deviation165901.45
Coefficient of variation (CV)0.016077506
Kurtosis-1.0934127
Mean10318854
Median Absolute Deviation (MAD)142677
Skewness-0.096123579
Sum2.053452 × 109
Variance2.7523291 × 1010
MonotonicityStrictly increasing
2023-12-10T15:46:27.025694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10003682 1
 
0.5%
10431700 1
 
0.5%
10395373 1
 
0.5%
10395773 1
 
0.5%
10397990 1
 
0.5%
10398986 1
 
0.5%
10399886 1
 
0.5%
10402553 1
 
0.5%
10403730 1
 
0.5%
10406144 1
 
0.5%
Other values (189) 189
95.0%
ValueCountFrequency (%)
10003682 1
0.5%
10003921 1
0.5%
10009016 1
0.5%
10013115 1
0.5%
10021471 1
0.5%
10021493 1
0.5%
10023016 1
0.5%
10025748 1
0.5%
10028807 1
0.5%
10044560 1
0.5%
ValueCountFrequency (%)
10605375 1
0.5%
10604231 1
0.5%
10603645 1
0.5%
10602454 1
0.5%
10586213 1
0.5%
10581318 1
0.5%
10574521 1
0.5%
10572265 1
0.5%
10567471 1
0.5%
10565923 1
0.5%

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing199
Missing (%)100.0%
Memory size1.9 KiB

30-Aug-2014
Date

Distinct106
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum1988-06-14 00:00:00
Maximum2015-01-06 00:00:00
2023-12-10T15:46:27.191720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:27.333884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing199
Missing (%)100.0%
Memory size1.9 KiB

30-Sep-2019
Date

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2019-09-30 00:00:00
Maximum2019-09-30 00:00:00
2023-12-10T15:46:27.451723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:27.536986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

AA3
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
AA3
199 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAA3
2nd rowAA3
3rd rowAA3
4th rowAA3
5th rowAA3

Common Values

ValueCountFrequency (%)
AA3 199
100.0%

Length

2023-12-10T15:46:27.660522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:27.793901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
aa3 199
100.0%

행정동추정임대료
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
행정동추정임대료
199 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
행정동추정임대료 199
100.0%

Length

2023-12-10T15:46:27.897060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:27.993074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
행정동추정임대료 199
100.0%

98700
Real number (ℝ)

Distinct50
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31883.92
Minimum5300
Maximum98700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:46:28.114910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5300
5-th percentile8700
Q112800
median18100
Q346100
95-th percentile98700
Maximum98700
Range93400
Interquartile range (IQR)33300

Descriptive statistics

Standard deviation27890.514
Coefficient of variation (CV)0.87475172
Kurtosis0.5924883
Mean31883.92
Median Absolute Deviation (MAD)8100
Skewness1.3476065
Sum6344900
Variance7.7788075 × 108
MonotonicityNot monotonic
2023-12-10T15:46:28.265374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98700 16
 
8.0%
14200 14
 
7.0%
46100 13
 
6.5%
10000 8
 
4.0%
12600 7
 
3.5%
18100 7
 
3.5%
9600 7
 
3.5%
38300 7
 
3.5%
18500 6
 
3.0%
10800 6
 
3.0%
Other values (40) 108
54.3%
ValueCountFrequency (%)
5300 1
 
0.5%
6700 3
 
1.5%
8100 1
 
0.5%
8500 4
2.0%
8700 3
 
1.5%
9600 7
3.5%
9700 5
2.5%
9900 1
 
0.5%
10000 8
4.0%
10400 1
 
0.5%
ValueCountFrequency (%)
98700 16
8.0%
95000 2
 
1.0%
82900 6
 
3.0%
68300 4
 
2.0%
64200 6
 
3.0%
54000 2
 
1.0%
52600 1
 
0.5%
48500 1
 
0.5%
46100 13
6.5%
46000 3
 
1.5%

Interactions

2023-12-10T15:46:25.303880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:24.567185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:24.933697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:25.417114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:24.700704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:25.061110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:25.530542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:24.838925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:25.174427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:46:28.368271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1000156710001567.198700
100015671.0001.0000.000
10001567.11.0001.0000.000
987000.0000.0001.000
2023-12-10T15:46:28.498335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1000156710001567.198700
100015671.0001.000-0.052
10001567.11.0001.000-0.052
98700-0.052-0.0521.000

Missing values

2023-12-10T15:46:25.683465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:46:25.839013image/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

2015011000156710001567.1Unnamed: 330-Aug-2014Unnamed: 530-Sep-2019AA3행정동추정임대료98700
02015011000368210003682<NA>30-Aug-2014<NA>30-Sep-2019AA3행정동추정임대료14200
12015011000392110003921<NA>30-Aug-2014<NA>30-Sep-2019AA3행정동추정임대료46100
22015011000901610009016<NA>30-Aug-2014<NA>30-Sep-2019AA3행정동추정임대료52600
32015011001311510013115<NA>30-Aug-2014<NA>30-Sep-2019AA3행정동추정임대료8500
42015011002147110021471<NA>30-Aug-2014<NA>30-Sep-2019AA3행정동추정임대료15000
52015011002149310021493<NA>31-Aug-2014<NA>30-Sep-2019AA3행정동추정임대료12600
62015011002301610023016<NA>31-Oct-2014<NA>30-Sep-2019AA3행정동추정임대료13900
72015011002574810025748<NA>30-Aug-2014<NA>30-Sep-2019AA3행정동추정임대료22400
82015011002880710028807<NA>31-Aug-2014<NA>30-Sep-2019AA3행정동추정임대료98700
92015011004456010044560<NA>30-Aug-2014<NA>30-Sep-2019AA3행정동추정임대료10000
2015011000156710001567.1Unnamed: 330-Aug-2014Unnamed: 530-Sep-2019AA3행정동추정임대료98700
1892015011056592310565923<NA>01-Feb-2014<NA>30-Sep-2019AA3행정동추정임대료29600
1902015011056747110567471<NA>09-Jan-2014<NA>30-Sep-2019AA3행정동추정임대료64200
1912015011057226510572265<NA>02-Dec-2013<NA>30-Sep-2019AA3행정동추정임대료6700
1922015011057452110574521<NA>16-Oct-2014<NA>30-Sep-2019AA3행정동추정임대료9600
1932015011058131810581318<NA>27-Oct-2014<NA>30-Sep-2019AA3행정동추정임대료14200
1942015011058621310586213<NA>02-Mar-2006<NA>30-Sep-2019AA3행정동추정임대료21200
1952015011060245410602454<NA>01-Feb-2005<NA>30-Sep-2019AA3행정동추정임대료13900
1962015011060364510603645<NA>26-Jan-2005<NA>30-Sep-2019AA3행정동추정임대료9700
1972015011060423110604231<NA>01-Feb-2005<NA>30-Sep-2019AA3행정동추정임대료12600
1982015011060537510605375<NA>02-Jul-2002<NA>30-Sep-2019AA3행정동추정임대료12600