Overview

Dataset statistics

Number of variables5
Number of observations425
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.4 KiB
Average record size in memory44.3 B

Variable types

Numeric4
Text1

Dataset

Description행정동_코드,행정동_명,엑스좌표_값,와이좌표_값,영역_면적
Author서울신용보증재단
URLhttps://data.seoul.go.kr/dataList/OA-22160/S/1/datasetView.do

Alerts

행정동_코드 is highly overall correlated with 와이좌표_값High correlation
와이좌표_값 is highly overall correlated with 행정동_코드High correlation
행정동_코드 has unique valuesUnique
영역_면적 has unique valuesUnique

Reproduction

Analysis started2024-05-04 07:17:07.440609
Analysis finished2024-05-04 07:17:13.625567
Duration6.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동_코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct425
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11433425
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T07:17:13.854084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11140582
Q111260655
median11440630
Q311590680
95-th percentile11710678
Maximum11740700
Range630185
Interquartile range (IQR)330025

Descriptive statistics

Standard deviation191726.29
Coefficient of variation (CV)0.016768929
Kurtosis-1.2638166
Mean11433425
Median Absolute Deviation (MAD)179940
Skewness-0.014718459
Sum4.8592056 × 109
Variance3.6758969 × 1010
MonotonicityNot monotonic
2024-05-04T07:17:14.321466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11140520 1
 
0.2%
11590530 1
 
0.2%
11590510 1
 
0.2%
11560720 1
 
0.2%
11560710 1
 
0.2%
11560700 1
 
0.2%
11560690 1
 
0.2%
11560680 1
 
0.2%
11560670 1
 
0.2%
11560660 1
 
0.2%
Other values (415) 415
97.6%
ValueCountFrequency (%)
11110515 1
0.2%
11110530 1
0.2%
11110540 1
0.2%
11110550 1
0.2%
11110560 1
0.2%
11110570 1
0.2%
11110580 1
0.2%
11110600 1
0.2%
11110615 1
0.2%
11110630 1
0.2%
ValueCountFrequency (%)
11740700 1
0.2%
11740690 1
0.2%
11740685 1
0.2%
11740660 1
0.2%
11740650 1
0.2%
11740640 1
0.2%
11740620 1
0.2%
11740610 1
0.2%
11740600 1
0.2%
11740590 1
0.2%

행정동_명
Text

Distinct424
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2024-05-04T07:17:15.065629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.7882353
Min length2

Characters and Unicode

Total characters1610
Distinct characters188
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique423 ?
Unique (%)99.5%

Sample

1st row소공동
2nd row청운효자동
3rd row사직동
4th row삼청동
5th row부암동
ValueCountFrequency (%)
신사동 2
 
0.5%
노량진1동 1
 
0.2%
대림3동 1
 
0.2%
대림2동 1
 
0.2%
대림1동 1
 
0.2%
신길7동 1
 
0.2%
신길6동 1
 
0.2%
신길5동 1
 
0.2%
신길4동 1
 
0.2%
신길3동 1
 
0.2%
Other values (414) 414
97.4%
2024-05-04T07:17:16.145926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 427
26.5%
2 97
 
6.0%
1 97
 
6.0%
3 43
 
2.7%
신 38
 
2.4%
4 26
 
1.6%
가 23
 
1.4%
곡 18
 
1.1%
화 17
 
1.1%
계 17
 
1.1%
Other values (178) 807
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1309
81.3%
Decimal Number 292
 
18.1%
Other Punctuation 9
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 427
32.6%
신 38
 
2.9%
가 23
 
1.8%
곡 18
 
1.4%
화 17
 
1.3%
계 17
 
1.3%
산 16
 
1.2%
방 16
 
1.2%
상 16
 
1.2%
성 16
 
1.2%
Other values (167) 705
53.9%
Decimal Number
ValueCountFrequency (%)
2 97
33.2%
1 97
33.2%
3 43
14.7%
4 26
 
8.9%
5 11
 
3.8%
6 7
 
2.4%
7 6
 
2.1%
8 3
 
1.0%
9 1
 
0.3%
0 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
? 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1309
81.3%
Common 301
 
18.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 427
32.6%
신 38
 
2.9%
가 23
 
1.8%
곡 18
 
1.4%
화 17
 
1.3%
계 17
 
1.3%
산 16
 
1.2%
방 16
 
1.2%
상 16
 
1.2%
성 16
 
1.2%
Other values (167) 705
53.9%
Common
ValueCountFrequency (%)
2 97
32.2%
1 97
32.2%
3 43
14.3%
4 26
 
8.6%
5 11
 
3.7%
? 9
 
3.0%
6 7
 
2.3%
7 6
 
2.0%
8 3
 
1.0%
9 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1309
81.3%
ASCII 301
 
18.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 427
32.6%
신 38
 
2.9%
가 23
 
1.8%
곡 18
 
1.4%
화 17
 
1.3%
계 17
 
1.3%
산 16
 
1.2%
방 16
 
1.2%
상 16
 
1.2%
성 16
 
1.2%
Other values (167) 705
53.9%
ASCII
ValueCountFrequency (%)
2 97
32.2%
1 97
32.2%
3 43
14.3%
4 26
 
8.6%
5 11
 
3.7%
? 9
 
3.0%
6 7
 
2.3%
7 6
 
2.0%
8 3
 
1.0%
9 1
 
0.3%

엑스좌표_값
Real number (ℝ)

Distinct423
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199436.08
Minimum181853
Maximum215398
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T07:17:16.644679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum181853
5-th percentile186448.2
Q1193052
median200389
Q3205399
95-th percentile211529.2
Maximum215398
Range33545
Interquartile range (IQR)12347

Descriptive statistics

Standard deviation7632.0366
Coefficient of variation (CV)0.038268083
Kurtosis-0.87858509
Mean199436.08
Median Absolute Deviation (MAD)5978
Skewness-0.15229199
Sum84760335
Variance58247982
MonotonicityNot monotonic
2024-05-04T07:17:17.115216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206626 2
 
0.5%
193559 2
 
0.5%
190331 1
 
0.2%
195487 1
 
0.2%
190905 1
 
0.2%
191201 1
 
0.2%
191618 1
 
0.2%
192893 1
 
0.2%
192350 1
 
0.2%
191645 1
 
0.2%
Other values (413) 413
97.2%
ValueCountFrequency (%)
181853 1
0.2%
182690 1
0.2%
183884 1
0.2%
183888 1
0.2%
184486 1
0.2%
184559 1
0.2%
184615 1
0.2%
184750 1
0.2%
184926 1
0.2%
185025 1
0.2%
ValueCountFrequency (%)
215398 1
0.2%
214565 1
0.2%
214314 1
0.2%
213796 1
0.2%
213634 1
0.2%
213336 1
0.2%
213321 1
0.2%
213115 1
0.2%
213015 1
0.2%
212900 1
0.2%

와이좌표_값
Real number (ℝ)

HIGH CORRELATION 

Distinct422
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450012.68
Minimum437858
Maximum465073
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T07:17:17.539670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437858
5-th percentile441681.6
Q1445118
median449786
Q3453874
95-th percentile460981.8
Maximum465073
Range27215
Interquartile range (IQR)8756

Descriptive statistics

Standard deviation5898.007
Coefficient of variation (CV)0.013106313
Kurtosis-0.54594904
Mean450012.68
Median Absolute Deviation (MAD)4479
Skewness0.35209019
Sum1.9125539 × 108
Variance34786487
MonotonicityNot monotonic
2024-05-04T07:17:17.959326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
447737 2
 
0.5%
451602 2
 
0.5%
456523 2
 
0.5%
451636 1
 
0.2%
445311 1
 
0.2%
444326 1
 
0.2%
445722 1
 
0.2%
445863 1
 
0.2%
444509 1
 
0.2%
443357 1
 
0.2%
Other values (412) 412
96.9%
ValueCountFrequency (%)
437858 1
0.2%
438677 1
0.2%
439058 1
0.2%
439083 1
0.2%
439227 1
0.2%
439279 1
0.2%
439607 1
0.2%
440076 1
0.2%
440161 1
0.2%
440235 1
0.2%
ValueCountFrequency (%)
465073 1
0.2%
464860 1
0.2%
464622 1
0.2%
464017 1
0.2%
463282 1
0.2%
463108 1
0.2%
462988 1
0.2%
462932 1
0.2%
462823 1
0.2%
462617 1
0.2%

영역_면적
Real number (ℝ)

UNIQUE 

Distinct425
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1425305.1
Minimum216835
Maximum12708880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T07:17:18.346359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum216835
5-th percentile447881.8
Q1668998
median974298
Q31508001
95-th percentile3116959.2
Maximum12708880
Range12492045
Interquartile range (IQR)839003

Descriptive statistics

Standard deviation1575644
Coefficient of variation (CV)1.1054784
Kurtosis19.984164
Mean1425305.1
Median Absolute Deviation (MAD)351898
Skewness4.1008804
Sum6.0575467 × 108
Variance2.482654 × 1012
MonotonicityNot monotonic
2024-05-04T07:17:18.808919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
943795 1
 
0.2%
1488648 1
 
0.2%
1613446 1
 
0.2%
921696 1
 
0.2%
572540 1
 
0.2%
498978 1
 
0.2%
642487 1
 
0.2%
653511 1
 
0.2%
494653 1
 
0.2%
292127 1
 
0.2%
Other values (415) 415
97.6%
ValueCountFrequency (%)
216835 1
0.2%
239396 1
0.2%
257323 1
0.2%
277531 1
0.2%
292127 1
0.2%
297155 1
0.2%
305337 1
0.2%
319706 1
0.2%
327045 1
0.2%
328640 1
0.2%
ValueCountFrequency (%)
12708880 1
0.2%
11565110 1
0.2%
10861768 1
0.2%
10587752 1
0.2%
8947328 1
0.2%
8451468 1
0.2%
8437543 1
0.2%
8405183 1
0.2%
8402415 1
0.2%
7562268 1
0.2%

Interactions

2024-05-04T07:17:12.011036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:17:08.093900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:17:09.613781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:17:10.761024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:17:12.262991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:17:08.485148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:17:09.916026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:17:11.049817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:17:12.552394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:17:08.790133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:17:10.200752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:17:11.442516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:17:12.812435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:17:09.291558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:17:10.480022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:17:11.836942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T07:17:19.074840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동_코드엑스좌표_값와이좌표_값영역_면적
행정동_코드1.0000.8940.8860.097
엑스좌표_값0.8941.0000.4650.132
와이좌표_값0.8860.4651.0000.287
영역_면적0.0970.1320.2871.000
2024-05-04T07:17:19.447109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동_코드엑스좌표_값와이좌표_값영역_면적
행정동_코드1.0000.012-0.6360.176
엑스좌표_값0.0121.0000.2130.059
와이좌표_값-0.6360.2131.000-0.031
영역_면적0.1760.059-0.0311.000

Missing values

2024-05-04T07:17:13.173686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T07:17:13.499346image/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

행정동_코드행정동_명엑스좌표_값와이좌표_값영역_면적
011140520소공동197736451636943795
111110515청운효자동1973424538742568432
211110530사직동1973834527051158536
311110540삼청동1983404543121479255
411110550부암동1967814552662274739
511110560평창동1971864573448947328
611110570무악동196342453063369520
711110580교남동196830452392346926
811110600가회동198812453662539955
911110615종로1?2?3?4가동1991044528492417171
행정동_코드행정동_명엑스좌표_값와이좌표_값영역_면적
41511680730일원1동207892443783953471
41611680740일원2동2069714440951300290
41711680750수서동2088574430571629403
41811710510풍납1동2101344487761075294
41911740690둔촌1동212413447073912178
42011740700둔촌2동2130154479891550455
42111740640성내1동211097447696663675
42211740650성내2동211287448330625147
42311740660성내3동211824447659647356
42411740685길동2129004489201631616