Overview

Dataset statistics

Number of variables4
Number of observations1138
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.8 KiB
Average record size in memory33.1 B

Variable types

Numeric1
Text2
Categorical1

Dataset

Description행정동_ID,행정동_명칭,자치구_명칭,시도_명칭
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21234/S/1/datasetView.do

Alerts

행정동_ID is highly overall correlated with 시도_명칭High correlation
시도_명칭 is highly overall correlated with 행정동_IDHigh correlation
행정동_ID has unique valuesUnique

Reproduction

Analysis started2024-05-04 03:29:49.923784
Analysis finished2024-05-04 03:29:50.990825
Duration1.07 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동_ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1138
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2311231.7
Minimum1101053
Maximum11250770
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-05-04T03:29:51.130993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101053
5-th percentile1104065.9
Q11119054.2
median2332035.5
Q33110455.8
95-th percentile3127032.1
Maximum11250770
Range10149717
Interquartile range (IQR)1991401.5

Descriptive statistics

Standard deviation1125287
Coefficient of variation (CV)0.48687764
Kurtosis18.445319
Mean2311231.7
Median Absolute Deviation (MAD)788021
Skewness2.3567676
Sum2.6301817 × 109
Variance1.2662709 × 1012
MonotonicityNot monotonic
2024-05-04T03:29:51.405181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3138041 1
 
0.1%
1124051 1
 
0.1%
1123075 1
 
0.1%
1123076 1
 
0.1%
1123077 1
 
0.1%
1123078 1
 
0.1%
1123079 1
 
0.1%
1123080 1
 
0.1%
1124052 1
 
0.1%
1123072 1
 
0.1%
Other values (1128) 1128
99.1%
ValueCountFrequency (%)
1101053 1
0.1%
1101054 1
0.1%
1101055 1
0.1%
1101056 1
0.1%
1101057 1
0.1%
1101058 1
0.1%
1101060 1
0.1%
1101061 1
0.1%
1101063 1
0.1%
1101064 1
0.1%
ValueCountFrequency (%)
11250770 1
0.1%
11250760 1
0.1%
11250550 1
0.1%
11230511 1
0.1%
11170740 1
0.1%
11170730 1
0.1%
3138041 1
0.1%
3138040 1
0.1%
3138039 1
0.1%
3138038 1
0.1%

행정동_명칭
Text

Distinct1102
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2024-05-04T03:29:51.949390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length3.5957821
Min length2

Characters and Unicode

Total characters4092
Distinct characters253
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

Unique1076 ?
Unique (%)94.6%

Sample

1st row개군면
2nd row용문면
3rd row지평면
4th row양동면
5th row청운면
ValueCountFrequency (%)
중앙동 9
 
0.8%
신촌동 3
 
0.3%
위례동 3
 
0.3%
금곡동 3
 
0.3%
신장1동 2
 
0.2%
정자1동 2
 
0.2%
신장2동 2
 
0.2%
신흥동 2
 
0.2%
논현1동 2
 
0.2%
군내면 2
 
0.2%
Other values (1092) 1108
97.4%
2024-05-04T03:29:52.953082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1000
24.4%
1 209
 
5.1%
2 203
 
5.0%
면 130
 
3.2%
3 101
 
2.5%
신 71
 
1.7%
산 66
 
1.6%
정 51
 
1.2%
4 48
 
1.2%
양 47
 
1.1%
Other values (243) 2166
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3467
84.7%
Decimal Number 610
 
14.9%
Other Punctuation 15
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1000
28.8%
면 130
 
3.7%
신 71
 
2.0%
산 66
 
1.9%
정 51
 
1.5%
양 47
 
1.4%
곡 46
 
1.3%
수 41
 
1.2%
성 38
 
1.1%
원 38
 
1.1%
Other values (232) 1939
55.9%
Decimal Number
ValueCountFrequency (%)
1 209
34.3%
2 203
33.3%
3 101
16.6%
4 48
 
7.9%
5 19
 
3.1%
6 13
 
2.1%
7 9
 
1.5%
8 5
 
0.8%
9 2
 
0.3%
0 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3467
84.7%
Common 625
 
15.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1000
28.8%
면 130
 
3.7%
신 71
 
2.0%
산 66
 
1.9%
정 51
 
1.5%
양 47
 
1.4%
곡 46
 
1.3%
수 41
 
1.2%
성 38
 
1.1%
원 38
 
1.1%
Other values (232) 1939
55.9%
Common
ValueCountFrequency (%)
1 209
33.4%
2 203
32.5%
3 101
16.2%
4 48
 
7.7%
5 19
 
3.0%
, 15
 
2.4%
6 13
 
2.1%
7 9
 
1.4%
8 5
 
0.8%
9 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3467
84.7%
ASCII 625
 
15.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1000
28.8%
면 130
 
3.7%
신 71
 
2.0%
산 66
 
1.9%
정 51
 
1.5%
양 47
 
1.4%
곡 46
 
1.3%
수 41
 
1.2%
성 38
 
1.1%
원 38
 
1.1%
Other values (232) 1939
55.9%
ASCII
ValueCountFrequency (%)
1 209
33.4%
2 203
32.5%
3 101
16.2%
4 48
 
7.7%
5 19
 
3.0%
, 15
 
2.4%
6 13
 
2.1%
7 9
 
1.4%
8 5
 
0.8%
9 2
 
0.3%

자치구_명칭
Text

Distinct83
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2024-05-04T03:29:53.390829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.543058
Min length2

Characters and Unicode

Total characters4032
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row양평군
2nd row양평군
3rd row양평군
4th row양평군
5th row양평군
ValueCountFrequency (%)
부천시 36
 
3.2%
중구 27
 
2.4%
송파구 27
 
2.4%
화성시 24
 
2.1%
부평구 22
 
1.9%
평택시 22
 
1.9%
강남구 22
 
1.9%
서구 21
 
1.8%
남구 21
 
1.8%
관악구 21
 
1.8%
Other values (73) 895
78.6%
2024-05-04T03:29:54.378509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 800
19.8%
시 529
 
13.1%
양 158
 
3.9%
남 137
 
3.4%
동 131
 
3.2%
성 122
 
3.0%
천 116
 
2.9%
안 104
 
2.6%
강 87
 
2.2%
서 82
 
2.0%
Other values (70) 1766
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4032
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 800
19.8%
시 529
 
13.1%
양 158
 
3.9%
남 137
 
3.4%
동 131
 
3.2%
성 122
 
3.0%
천 116
 
2.9%
안 104
 
2.6%
강 87
 
2.2%
서 82
 
2.0%
Other values (70) 1766
43.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4032
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 800
19.8%
시 529
 
13.1%
양 158
 
3.9%
남 137
 
3.4%
동 131
 
3.2%
성 122
 
3.0%
천 116
 
2.9%
안 104
 
2.6%
강 87
 
2.2%
서 82
 
2.0%
Other values (70) 1766
43.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4032
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 800
19.8%
시 529
 
13.1%
양 158
 
3.9%
남 137
 
3.4%
동 131
 
3.2%
성 122
 
3.0%
천 116
 
2.9%
안 104
 
2.6%
강 87
 
2.2%
서 82
 
2.0%
Other values (70) 1766
43.8%

시도_명칭
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
경기도
561 
서울
426 
인천
151 

Length

Max length3
Median length2
Mean length2.4929701
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 561
49.3%
서울 426
37.4%
인천 151
 
13.3%

Length

2024-05-04T03:29:54.817599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:29:55.141359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 561
49.3%
서울 426
37.4%
인천 151
 
13.3%

Interactions

2024-05-04T03:29:50.456578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T03:29:55.339588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동_ID자치구_명칭시도_명칭
행정동_ID1.0000.9630.679
자치구_명칭0.9631.0000.998
시도_명칭0.6790.9981.000
2024-05-04T03:29:55.634133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동_ID시도_명칭
행정동_ID1.0000.710
시도_명칭0.7101.000

Missing values

2024-05-04T03:29:50.773756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T03:29:50.930310image/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_ID행정동_명칭자치구_명칭시도_명칭
03138041개군면양평군경기도
13138040용문면양평군경기도
23138039지평면양평군경기도
33138038양동면양평군경기도
43138037청운면양평군경기도
53138036단월면양평군경기도
63138035서종면양평군경기도
73138034옥천면양평군경기도
83138033양서면양평군경기도
93138032강하면양평군경기도
행정동_ID행정동_명칭자치구_명칭시도_명칭
11281101064이화동종로구서울
11291101063종로5,6가동종로구서울
11301101061종로1,2,3,4가동종로구서울
11311101060가회동종로구서울
11321101058교남동종로구서울
11331101057무악동종로구서울
11341101056평창동종로구서울
11351101055부암동종로구서울
11361101054삼청동종로구서울
11371101053사직동종로구서울