Overview

Dataset statistics

Number of variables7
Number of observations134
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory59.0 B

Variable types

Text3
Categorical2
Numeric2

Dataset

Description키,표기명,행정 시,행정 구,행정 동,중심 좌표 X,중심 좌표 Y
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-12933/S/1/datasetView.do

Alerts

행정 시 has constant value ""Constant
중심 좌표 X is highly overall correlated with 행정 구High correlation
중심 좌표 Y is highly overall correlated with 행정 구High correlation
행정 구 is highly overall correlated with 중심 좌표 X and 1 other fieldsHigh correlation
키 has unique valuesUnique

Reproduction

Analysis started2023-12-11 03:50:37.593254
Analysis finished2023-12-11 03:50:38.597509
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

키
Text

UNIQUE 

Distinct134
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T12:50:38.772168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1608
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134 ?
Unique (%)100.0%

Sample

1st rowBE_IW01-0082
2nd rowBE_IW01-0083
3rd rowBE_IW01-0084
4th rowBE_IW01-0085
5th rowBE_IW01-0086
ValueCountFrequency (%)
be_iw01-0082 1
 
0.7%
be_iw01-0031 1
 
0.7%
be_iw01-0045 1
 
0.7%
be_iw01-0044 1
 
0.7%
be_iw01-0043 1
 
0.7%
be_iw01-0042 1
 
0.7%
be_iw01-0041 1
 
0.7%
be_iw01-0040 1
 
0.7%
be_iw01-0039 1
 
0.7%
be_iw01-0038 1
 
0.7%
Other values (124) 124
92.5%
2023-12-11T12:50:39.206515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 399
24.8%
1 203
12.6%
B 134
 
8.3%
E 134
 
8.3%
_ 134
 
8.3%
I 134
 
8.3%
W 134
 
8.3%
- 134
 
8.3%
2 34
 
2.1%
3 29
 
1.8%
Other values (6) 139
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 804
50.0%
Uppercase Letter 536
33.3%
Connector Punctuation 134
 
8.3%
Dash Punctuation 134
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 399
49.6%
1 203
25.2%
2 34
 
4.2%
3 29
 
3.6%
4 24
 
3.0%
8 23
 
2.9%
9 23
 
2.9%
5 23
 
2.9%
6 23
 
2.9%
7 23
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
B 134
25.0%
E 134
25.0%
I 134
25.0%
W 134
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 134
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1072
66.7%
Latin 536
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 399
37.2%
1 203
18.9%
_ 134
 
12.5%
- 134
 
12.5%
2 34
 
3.2%
3 29
 
2.7%
4 24
 
2.2%
8 23
 
2.1%
9 23
 
2.1%
5 23
 
2.1%
Other values (2) 46
 
4.3%
Latin
ValueCountFrequency (%)
B 134
25.0%
E 134
25.0%
I 134
25.0%
W 134
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 399
24.8%
1 203
12.6%
B 134
 
8.3%
E 134
 
8.3%
_ 134
 
8.3%
I 134
 
8.3%
W 134
 
8.3%
- 134
 
8.3%
2 34
 
2.1%
3 29
 
1.8%
Other values (6) 139
 
8.6%

표기명
Text

Distinct129
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T12:50:39.566089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length5.8432836
Min length3

Characters and Unicode

Total characters783
Distinct characters209
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)93.3%

Sample

1st row大林洞中?城
2nd row加里峰洞中?城
3rd row紫?洞中?城
4th row中央市?家具??
5th row仁寺洞文化??
ValueCountFrequency (%)
北岳山路 3
 
2.2%
之街 3
 
2.2%
步行路1 2
 
1.5%
步行路3 2
 
1.5%
大林洞中?城 1
 
0.7%
渡口自行?公 1
 
0.7%
新沙洞辣?安康?街 1
 
0.7%
路 1
 
0.7%
步行路2 1
 
0.7%
往十里牛?街 1
 
0.7%
Other values (118) 118
88.1%
2023-12-11T12:50:40.074958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
? 202
25.8%
街 70
 
8.9%
路 42
 
5.4%
洞 38
 
4.9%
大 14
 
1.8%
德 11
 
1.4%
食 10
 
1.3%
新 10
 
1.3%
之 10
 
1.3%
美 10
 
1.3%
Other values (199) 366
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 562
71.8%
Other Punctuation 202
 
25.8%
Lowercase Letter 8
 
1.0%
Decimal Number 7
 
0.9%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%
Uppercase Letter 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
街 70
 
12.5%
路 42
 
7.5%
洞 38
 
6.8%
大 14
 
2.5%
德 11
 
2.0%
食 10
 
1.8%
新 10
 
1.8%
之 10
 
1.8%
美 10
 
1.8%
村 9
 
1.6%
Other values (184) 338
60.1%
Lowercase Letter
ValueCountFrequency (%)
e 2
25.0%
l 2
25.0%
g 1
12.5%
n 1
12.5%
i 1
12.5%
b 1
12.5%
Decimal Number
ValueCountFrequency (%)
3 3
42.9%
1 2
28.6%
2 1
 
14.3%
0 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
? 202
100.0%
Open Punctuation
ValueCountFrequency (%)
（ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
） 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
W 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 553
70.6%
Common 212
 
27.1%
Hangul 9
 
1.1%
Latin 9
 
1.1%

Most frequent character per script

Han
ValueCountFrequency (%)
街 70
 
12.7%
路 42
 
7.6%
洞 38
 
6.9%
大 14
 
2.5%
德 11
 
2.0%
食 10
 
1.8%
新 10
 
1.8%
之 10
 
1.8%
美 10
 
1.8%
村 9
 
1.6%
Other values (181) 329
59.5%
Common
ValueCountFrequency (%)
? 202
95.3%
3 3
 
1.4%
1 2
 
0.9%
2 1
 
0.5%
0 1
 
0.5%
（ 1
 
0.5%
） 1
 
0.5%
- 1
 
0.5%
Latin
ValueCountFrequency (%)
e 2
22.2%
l 2
22.2%
W 1
11.1%
g 1
11.1%
n 1
11.1%
i 1
11.1%
b 1
11.1%
Hangul
ValueCountFrequency (%)
씉 6
66.7%
싓 2
 
22.2%
앒 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
CJK 551
70.4%
ASCII 219
 
28.0%
Hangul 9
 
1.1%
CJK Compat Ideographs 2
 
0.3%
None 2
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 202
92.2%
3 3
 
1.4%
e 2
 
0.9%
l 2
 
0.9%
1 2
 
0.9%
2 1
 
0.5%
0 1
 
0.5%
W 1
 
0.5%
- 1
 
0.5%
g 1
 
0.5%
Other values (3) 3
 
1.4%
CJK
ValueCountFrequency (%)
街 70
 
12.7%
路 42
 
7.6%
洞 38
 
6.9%
大 14
 
2.5%
德 11
 
2.0%
食 10
 
1.8%
新 10
 
1.8%
之 10
 
1.8%
美 10
 
1.8%
村 9
 
1.6%
Other values (180) 327
59.3%
Hangul
ValueCountFrequency (%)
씉 6
66.7%
싓 2
 
22.2%
앒 1
 
11.1%
CJK Compat Ideographs
ValueCountFrequency (%)
梨 2
100.0%
None
ValueCountFrequency (%)
（ 1
50.0%
） 1
50.0%

행정 시
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
首?特?市
134 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row首?特?市
2nd row首?特?市
3rd row首?特?市
4th row首?特?市
5th row首?特?市

Common Values

ValueCountFrequency (%)
首?特?市 134
100.0%

Length

2023-12-11T12:50:40.228158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:50:40.350651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
首?特?市 134
100.0%

행정 구
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
?路?
22 
中?
20 
?川?
11 
瑞草?
7 
麻浦?
7 
Other values (18)
67 

Length

Max length4
Median length3
Mean length2.9477612
Min length2

Unique

Unique4 ?
Unique (%)3.0%

Sample

1st row永登浦?
2nd row九老?
3rd row?津?
4th row中?
5th row?路?

Common Values

ValueCountFrequency (%)
?路? 22
16.4%
中? 20
14.9%
?川? 11
 
8.2%
瑞草? 7
 
5.2%
麻浦? 7
 
5.2%
江南? 7
 
5.2%
西大?? 6
 
4.5%
江?? 6
 
4.5%
?山? 6
 
4.5%
永登浦? 5
 
3.7%
Other values (13) 37
27.6%

Length

2023-12-11T12:50:40.462991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
路 22
16.4%
中 20
14.9%
川 11
 
8.2%
瑞草 7
 
5.2%
麻浦 7
 
5.2%
江南 7
 
5.2%
西大 6
 
4.5%
江 6
 
4.5%
山 6
 
4.5%
冠岳 5
 
3.7%
Other values (13) 37
27.6%

행정 동
Text

Distinct69
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T12:50:40.702617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.6641791
Min length2

Characters and Unicode

Total characters491
Distinct characters90
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)27.6%

Sample

1st row大林3洞
2nd row加里峰洞
3rd row紫?1洞
4th row??洞
5th row?路1.2.3.4街洞
ValueCountFrequency (%)
洞 8
 
6.0%
木5洞 7
 
5.2%
光熙洞 5
 
3.7%
路1.2.3.4街洞 5
 
3.7%
明洞 4
 
3.0%
三?洞 4
 
3.0%
仁水洞 4
 
3.0%
平?洞 4
 
3.0%
新村洞 4
 
3.0%
新亭6洞 4
 
3.0%
Other values (58) 85
63.4%
2023-12-11T12:50:41.094996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
洞 134
27.3%
? 70
 
14.3%
2 19
 
3.9%
. 16
 
3.3%
1 13
 
2.6%
3 12
 
2.4%
路 9
 
1.8%
新 9
 
1.8%
5 8
 
1.6%
4 8
 
1.6%
Other values (80) 193
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 340
69.2%
Other Punctuation 86
 
17.5%
Decimal Number 65
 
13.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
洞 134
39.4%
路 9
 
2.6%
新 9
 
2.6%
街 7
 
2.1%
木 7
 
2.1%
三 7
 
2.1%
亭 7
 
2.1%
光 6
 
1.8%
岩 6
 
1.8%
村 5
 
1.5%
Other values (72) 143
42.1%
Decimal Number
ValueCountFrequency (%)
2 19
29.2%
1 13
20.0%
3 12
18.5%
5 8
12.3%
4 8
12.3%
6 5
 
7.7%
Other Punctuation
ValueCountFrequency (%)
? 70
81.4%
. 16
 
18.6%

Most occurring scripts

ValueCountFrequency (%)
Han 338
68.8%
Common 151
30.8%
Hangul 2
 
0.4%

Most frequent character per script

Han
ValueCountFrequency (%)
洞 134
39.6%
路 9
 
2.7%
新 9
 
2.7%
街 7
 
2.1%
木 7
 
2.1%
三 7
 
2.1%
亭 7
 
2.1%
光 6
 
1.8%
岩 6
 
1.8%
村 5
 
1.5%
Other values (71) 141
41.7%
Common
ValueCountFrequency (%)
? 70
46.4%
2 19
 
12.6%
. 16
 
10.6%
1 13
 
8.6%
3 12
 
7.9%
5 8
 
5.3%
4 8
 
5.3%
6 5
 
3.3%
Hangul
ValueCountFrequency (%)
쒧 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 338
68.8%
ASCII 151
30.8%
Hangul 2
 
0.4%

Most frequent character per block

CJK
ValueCountFrequency (%)
洞 134
39.6%
路 9
 
2.7%
新 9
 
2.7%
街 7
 
2.1%
木 7
 
2.1%
三 7
 
2.1%
亭 7
 
2.1%
光 6
 
1.8%
岩 6
 
1.8%
村 5
 
1.5%
Other values (71) 141
41.7%
ASCII
ValueCountFrequency (%)
? 70
46.4%
2 19
 
12.6%
. 16
 
10.6%
1 13
 
8.6%
3 12
 
7.9%
5 8
 
5.3%
4 8
 
5.3%
6 5
 
3.3%
Hangul
ValueCountFrequency (%)
쒧 2
100.0%

중심 좌표 X
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.98276
Minimum126.81332
Maximum127.14969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T12:50:41.248567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.81332
5-th percentile126.87259
Q1126.94655
median126.98738
Q3127.01214
95-th percentile127.12363
Maximum127.14969
Range0.33636955
Interquartile range (IQR)0.065589285

Descriptive statistics

Standard deviation0.067896268
Coefficient of variation (CV)0.00053468888
Kurtosis0.11777252
Mean126.98276
Median Absolute Deviation (MAD)0.03322717
Skewness0.15092863
Sum17015.69
Variance0.0046099033
MonotonicityNot monotonic
2023-12-11T12:50:41.426711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8758936853 7
 
5.2%
126.9959254377 5
 
3.7%
126.9465464425 4
 
3.0%
126.9803912024 4
 
3.0%
127.0045998616 4
 
3.0%
126.957402192 4
 
3.0%
126.8629865421 4
 
3.0%
126.9873802368 4
 
3.0%
126.9788194313 4
 
3.0%
126.948985568 3
 
2.2%
Other values (63) 91
67.9%
ValueCountFrequency (%)
126.8133225984 1
 
0.7%
126.8629865421 4
3.0%
126.8725938165 3
2.2%
126.8758936853 7
5.2%
126.8842347738 2
 
1.5%
126.8873071257 1
 
0.7%
126.8886175026 1
 
0.7%
126.8984840296 1
 
0.7%
126.9012770519 1
 
0.7%
126.9115342551 1
 
0.7%
ValueCountFrequency (%)
127.1496921479 1
0.7%
127.1470468799 1
0.7%
127.1308327064 1
0.7%
127.128897657 1
0.7%
127.1266790476 2
1.5%
127.126461696 1
0.7%
127.1221039568 1
0.7%
127.1040253187 1
0.7%
127.087693357 1
0.7%
127.0824623102 1
0.7%

중심 좌표 Y
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.552967
Minimum37.463202
Maximum37.63868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T12:50:41.599477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.463202
5-th percentile37.482903
Q137.526133
median37.55958
Q337.576449
95-th percentile37.61982
Maximum37.63868
Range0.17547813
Interquartile range (IQR)0.050316455

Descriptive statistics

Standard deviation0.03998042
Coefficient of variation (CV)0.0010646408
Kurtosis-0.22376734
Mean37.552967
Median Absolute Deviation (MAD)0.023229553
Skewness-0.045167647
Sum5032.0975
Variance0.001598434
MonotonicityNot monotonic
2023-12-11T12:50:41.744037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5365973016 7
 
5.2%
37.5613498068 5
 
3.7%
37.5611107256 4
 
3.0%
37.5828099517 4
 
3.0%
37.6370942943 4
 
3.0%
37.6178956805 4
 
3.0%
37.516577197 4
 
3.0%
37.5744028289 4
 
3.0%
37.568058597 4
 
3.0%
37.4829408135 3
 
2.2%
Other values (63) 91
67.9%
ValueCountFrequency (%)
37.4632016047 2
1.5%
37.475038316 2
1.5%
37.4787998297 2
1.5%
37.482833211 1
 
0.7%
37.4829408135 3
2.2%
37.4854043468 1
 
0.7%
37.4885117081 1
 
0.7%
37.4967500548 1
 
0.7%
37.4998663454 1
 
0.7%
37.5006879112 1
 
0.7%
ValueCountFrequency (%)
37.6386797307 1
 
0.7%
37.6370942943 4
3.0%
37.6305927195 1
 
0.7%
37.6209706338 1
 
0.7%
37.6192011241 1
 
0.7%
37.6178956805 4
3.0%
37.6168942335 1
 
0.7%
37.5971607572 2
1.5%
37.59692086 2
1.5%
37.592642472 1
 
0.7%

Interactions

2023-12-11T12:50:38.160558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:50:37.943234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:50:38.276909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:50:38.054182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:50:41.840415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정 구행정 동중심 좌표 X중심 좌표 Y
행정 구1.0001.0000.9530.918
행정 동1.0001.0000.9980.999
중심 좌표 X0.9530.9981.0000.627
중심 좌표 Y0.9180.9990.6271.000
2023-12-11T12:50:41.933950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중심 좌표 X중심 좌표 Y행정 구
중심 좌표 X1.0000.0250.729
중심 좌표 Y0.0251.0000.628
행정 구0.7290.6281.000

Missing values

2023-12-11T12:50:38.415710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:50:38.554765image/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

키표기명행정 시행정 구행정 동중심 좌표 X중심 좌표 Y
0BE_IW01-0082大林洞中?城首?特?市永登浦?大林3洞126.90127737.49675
1BE_IW01-0083加里峰洞中?城首?特?市九老?加里峰洞126.88730737.482833
2BE_IW01-0084紫?洞中?城首?特?市?津?紫?1洞127.08021537.531921
3BE_IW01-0085中央市?家具??首?特?市中???洞127.02043437.56861
4BE_IW01-0086仁寺洞文化??首?特?市?路??路1.2.3.4街洞126.9873837.574403
5BE_IW01-0087弘大??之街首?特?市麻浦?大?洞126.93792837.554241
6BE_IW01-0088新村梨大街首?特?市西大??新村洞126.94654637.561111
7BE_IW01-0089桂?路首?特?市?路?嘉?洞126.98480737.582612
8BE_IW01-0090冠岳??街首?特?市冠岳?中央洞126.94898637.482941
9BE_IW01-0091建大美食街首?特?市?津???洞127.07273637.543322
키표기명행정 시행정 구행정 동중심 좌표 X중심 좌표 Y
124BE_IW01-0072?洞街首?特?市中?小公洞126.97993437.56413
125BE_IW01-0073?熙大?앒?街首?特?市?大??徽?2洞127.06259937.588832
126BE_IW01-0074景福?后街首?特?市?路?社稷洞126.97262637.57839
127BE_IW01-0075別宮路首?特?市?路?三?洞126.98039137.58281
128BE_IW01-0076??洞????街首?特?市江南???2洞127.03119637.513636
129BE_IW01-0077菲律?街首?特?市?路??路1.2.3.4街洞126.9929937.58199
130BE_IW01-0078孔德洞猪蹄街首?特?市麻浦?阿?洞126.95608937.554602
131BE_IW01-0079千?洞?德??씉街首?特?市江??千?2洞127.12667937.543179
132BE_IW01-0080川久保玲街首?特?市?山??南洞127.00592937.535397
133BE_IW01-0081?大???坊首?特?市?路??路5.6街洞127.0001937.57369