Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory45.3 B

Variable types

Text1
Numeric4

Dataset

Description샘플 데이터
Author서울시, 신한카드, KCB(코리아크레딧뷰로)
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=321

Alerts

자가거주가구비율(INDEX01_RT) has 15 (15.0%) zerosZeros
자가거주지수(INDEX01) has 10 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-10 15:01:44.159412
Analysis finished2023-12-10 15:01:47.975514
Duration3.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

서울시_블록ID(BLK_CD)
Text

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-11T00:01:48.398880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.75
Min length4

Characters and Unicode

Total characters575
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)82.0%

Sample

1st row9*5*
2nd row2*7*7*
3rd row1*7*9*
4th row2*0*3*
5th row4*2*7*
ValueCountFrequency (%)
4*2*9 2
 
2.0%
2*9*8 2
 
2.0%
2*3*0 2
 
2.0%
2*0*3 2
 
2.0%
2*9*9 2
 
2.0%
2*5*0 2
 
2.0%
2*2*8 2
 
2.0%
2*7*2 2
 
2.0%
2*7*7 2
 
2.0%
1*7*4 2
 
2.0%
Other values (80) 80
80.0%
2023-12-11T00:01:49.229350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 279
48.5%
2 69
 
12.0%
3 43
 
7.5%
1 38
 
6.6%
7 27
 
4.7%
4 26
 
4.5%
9 21
 
3.7%
0 20
 
3.5%
5 20
 
3.5%
8 17
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 296
51.5%
Other Punctuation 279
48.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 69
23.3%
3 43
14.5%
1 38
12.8%
7 27
 
9.1%
4 26
 
8.8%
9 21
 
7.1%
0 20
 
6.8%
5 20
 
6.8%
8 17
 
5.7%
6 15
 
5.1%
Other Punctuation
ValueCountFrequency (%)
* 279
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 575
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 279
48.5%
2 69
 
12.0%
3 43
 
7.5%
1 38
 
6.6%
7 27
 
4.7%
4 26
 
4.5%
9 21
 
3.7%
0 20
 
3.5%
5 20
 
3.5%
8 17
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 575
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 279
48.5%
2 69
 
12.0%
3 43
 
7.5%
1 38
 
6.6%
7 27
 
4.7%
4 26
 
4.5%
9 21
 
3.7%
0 20
 
3.5%
5 20
 
3.5%
8 17
 
3.0%

가구수(S_GAGU_CNT)
Real number (ℝ)

Distinct74
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.43
Minimum1
Maximum828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T00:01:49.509801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.7
Q134
median60
Q3117.25
95-th percentile203.35
Maximum828
Range827
Interquartile range (IQR)83.25

Descriptive statistics

Standard deviation93.629428
Coefficient of variation (CV)1.1089592
Kurtosis40.099562
Mean84.43
Median Absolute Deviation (MAD)33
Skewness5.333307
Sum8443
Variance8766.4698
MonotonicityNot monotonic
2023-12-11T00:01:49.687624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 5
 
5.0%
58 3
 
3.0%
34 3
 
3.0%
60 3
 
3.0%
126 3
 
3.0%
27 3
 
3.0%
22 2
 
2.0%
79 2
 
2.0%
119 2
 
2.0%
53 2
 
2.0%
Other values (64) 72
72.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
14 1
1.0%
20 2
2.0%
21 2
2.0%
22 2
2.0%
23 1
1.0%
24 2
2.0%
ValueCountFrequency (%)
828 1
1.0%
267 1
1.0%
247 1
1.0%
239 1
1.0%
210 1
1.0%
203 1
1.0%
173 1
1.0%
167 1
1.0%
165 1
1.0%
155 1
1.0%

인구수(S_POP_CNT)
Real number (ℝ)

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191.7
Minimum1
Maximum2176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T00:01:49.899513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33.95
Q183.5
median142
Q3231.25
95-th percentile495.25
Maximum2176
Range2175
Interquartile range (IQR)147.75

Descriptive statistics

Standard deviation237.4231
Coefficient of variation (CV)1.2385138
Kurtosis49.593534
Mean191.7
Median Absolute Deviation (MAD)74.5
Skewness6.152862
Sum19170
Variance56369.727
MonotonicityNot monotonic
2023-12-11T00:01:50.097147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 3
 
3.0%
195 3
 
3.0%
104 2
 
2.0%
39 2
 
2.0%
179 2
 
2.0%
112 2
 
2.0%
51 2
 
2.0%
31 2
 
2.0%
95 2
 
2.0%
46 2
 
2.0%
Other values (77) 78
78.0%
ValueCountFrequency (%)
1 1
1.0%
31 2
2.0%
32 1
1.0%
33 1
1.0%
34 1
1.0%
38 1
1.0%
39 2
2.0%
46 2
2.0%
51 2
2.0%
52 1
1.0%
ValueCountFrequency (%)
2176 1
1.0%
575 1
1.0%
527 1
1.0%
519 1
1.0%
500 1
1.0%
495 1
1.0%
474 1
1.0%
460 1
1.0%
459 1
1.0%
406 1
1.0%

자가거주가구비율(INDEX01_RT)
Real number (ℝ)

ZEROS 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.283331
Minimum0
Maximum0.9683
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T00:01:50.306242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0784
median0.2115
Q30.497225
95-th percentile0.694745
Maximum0.9683
Range0.9683
Interquartile range (IQR)0.418825

Descriptive statistics

Standard deviation0.25038727
Coefficient of variation (CV)0.88372704
Kurtosis-0.53555149
Mean0.283331
Median Absolute Deviation (MAD)0.16475
Skewness0.73798084
Sum28.3331
Variance0.062693783
MonotonicityNot monotonic
2023-12-11T00:01:50.504044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
15.0%
0.1579 3
 
3.0%
0.6275 2
 
2.0%
0.2 2
 
2.0%
0.4167 2
 
2.0%
0.0526 1
 
1.0%
0.3188 1
 
1.0%
0.1567 1
 
1.0%
0.5114 1
 
1.0%
0.3272 1
 
1.0%
Other values (71) 71
71.0%
ValueCountFrequency (%)
0.0 15
15.0%
0.0178 1
 
1.0%
0.0345 1
 
1.0%
0.0364 1
 
1.0%
0.0385 1
 
1.0%
0.0455 1
 
1.0%
0.0526 1
 
1.0%
0.0566 1
 
1.0%
0.058 1
 
1.0%
0.0638 1
 
1.0%
ValueCountFrequency (%)
0.9683 1
1.0%
0.8732 1
1.0%
0.8043 1
1.0%
0.766 1
1.0%
0.7412 1
1.0%
0.6923 1
1.0%
0.6912 1
1.0%
0.6833 1
1.0%
0.6825 1
1.0%
0.6667 1
1.0%

자가거주지수(INDEX01)
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.47
Minimum0
Maximum9
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T00:01:50.658074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8690441
Coefficient of variation (CV)0.64184432
Kurtosis-1.2726673
Mean4.47
Median Absolute Deviation (MAD)3
Skewness-0.0429483
Sum447
Variance8.2314141
MonotonicityNot monotonic
2023-12-11T00:01:50.787529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 16
16.0%
3 14
14.0%
1 12
12.0%
0 10
10.0%
8 9
9.0%
4 9
9.0%
6 9
9.0%
9 8
8.0%
5 7
7.0%
2 6
 
6.0%
ValueCountFrequency (%)
0 10
10.0%
1 12
12.0%
2 6
 
6.0%
3 14
14.0%
4 9
9.0%
5 7
7.0%
6 9
9.0%
7 16
16.0%
8 9
9.0%
9 8
8.0%
ValueCountFrequency (%)
9 8
8.0%
8 9
9.0%
7 16
16.0%
6 9
9.0%
5 7
7.0%
4 9
9.0%
3 14
14.0%
2 6
 
6.0%
1 12
12.0%
0 10
10.0%

Interactions

2023-12-11T00:01:46.737103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:44.454415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:45.194530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:45.955186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:46.967908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:44.611864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:45.418424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:46.135222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:47.170382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:44.780388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:45.649385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:46.310358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:47.370741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:44.992531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:45.812056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:01:46.509845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T00:01:51.216758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서울시_블록ID(BLK_CD)가구수(S_GAGU_CNT)인구수(S_POP_CNT)자가거주가구비율(INDEX01_RT)자가거주지수(INDEX01)
서울시_블록ID(BLK_CD)1.0000.8840.6870.0000.000
가구수(S_GAGU_CNT)0.8841.0000.0860.1990.365
인구수(S_POP_CNT)0.6870.0861.0000.0000.141
자가거주가구비율(INDEX01_RT)0.0000.1990.0001.0000.000
자가거주지수(INDEX01)0.0000.3650.1410.0001.000
2023-12-11T00:01:51.358443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가구수(S_GAGU_CNT)인구수(S_POP_CNT)자가거주가구비율(INDEX01_RT)자가거주지수(INDEX01)
가구수(S_GAGU_CNT)1.000-0.0340.1000.059
인구수(S_POP_CNT)-0.0341.0000.0530.096
자가거주가구비율(INDEX01_RT)0.1000.0531.000-0.036
자가거주지수(INDEX01)0.0590.096-0.0361.000

Missing values

2023-12-11T00:01:47.661941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T00:01:47.896060image/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

서울시_블록ID(BLK_CD)가구수(S_GAGU_CNT)인구수(S_POP_CNT)자가거주가구비율(INDEX01_RT)자가거주지수(INDEX01)
09*5*582300.03
12*7*7*2031770.32268
21*7*9*241730.29031
32*0*3*471960.23189
44*2*7*52520.87323
51*2*0651120.56964
62*5*3*341890.13895
78*3*875000.23531
81*8*7461410.56674
92*9*7*130950.05
서울시_블록ID(BLK_CD)가구수(S_GAGU_CNT)인구수(S_POP_CNT)자가거주가구비율(INDEX01_RT)자가거주지수(INDEX01)
902*3*1*492170.25844
912*1*6*60310.01786
921*3*9117310.26397
932*6*1*601510.09
943*0*7*3380.41678
954*9*3*244060.69129
962*5*6*56560.16134
971*5*7*59320.19051
982*6*2*140990.03648
991*7*5*271790.01