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,표기용 이름,행정 시,행정 구,행정 동,중심 좌표 X,중심 좌표 Y
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-12931/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 06:40:54.292882
Analysis finished2023-12-11 06:40:54.991228
Duration0.7 seconds
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
2023-12-11T15:40:55.172186image/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_IW01-0085
2nd rowBE_IW01-0086
3rd rowBE_IW01-0087
4th rowBE_IW01-0088
5th rowBE_IW01-0089
ValueCountFrequency (%)
be_iw01-0085 1
 
0.7%
be_iw01-0034 1
 
0.7%
be_iw01-0048 1
 
0.7%
be_iw01-0047 1
 
0.7%
be_iw01-0046 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%
Other values (124) 124
92.5%
2023-12-11T15:40:55.546770image/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%
5 23
 
2.9%
9 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%
5 23
 
2.1%
9 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%

표기용 이름
Text

Distinct122
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T15:40:55.794537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length33
Mean length20.171642
Min length9

Characters and Unicode

Total characters2703
Distinct characters58
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique118 ?
Unique (%)88.1%

Sample

1st rowmarket housing development
2nd rowInsadong Culture Plaza
3rd rowHongik Univ Street of Art
4th rowSinchon Ewha Street
5th rowGyedong-gil
ValueCountFrequency (%)
street 50
 
13.4%
alley 20
 
5.3%
of 14
 
3.7%
the 12
 
3.2%
food 8
 
2.1%
rodeo 7
 
1.9%
course 6
 
1.6%
road 5
 
1.3%
chinatown 5
 
1.3%
village 5
 
1.3%
Other values (198) 242
64.7%
2023-12-11T15:40:56.199149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 314
 
11.6%
241
 
8.9%
o 210
 
7.8%
n 181
 
6.7%
a 174
 
6.4%
t 166
 
6.1%
l 145
 
5.4%
g 140
 
5.2%
r 118
 
4.4%
i 110
 
4.1%
Other values (48) 904
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2144
79.3%
Uppercase Letter 250
 
9.2%
Space Separator 241
 
8.9%
Dash Punctuation 54
 
2.0%
Other Punctuation 5
 
0.2%
Decimal Number 5
 
0.2%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 314
14.6%
o 210
9.8%
n 181
 
8.4%
a 174
 
8.1%
t 166
 
7.7%
l 145
 
6.8%
g 140
 
6.5%
r 118
 
5.5%
i 110
 
5.1%
s 88
 
4.1%
Other values (16) 498
23.2%
Uppercase Letter
ValueCountFrequency (%)
S 57
22.8%
C 24
9.6%
A 23
9.2%
B 18
 
7.2%
R 18
 
7.2%
T 15
 
6.0%
G 11
 
4.4%
M 11
 
4.4%
D 10
 
4.0%
H 10
 
4.0%
Other values (13) 53
21.2%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
3 2
40.0%
2 1
20.0%
Other Punctuation
ValueCountFrequency (%)
? 4
80.0%
' 1
 
20.0%
Space Separator
ValueCountFrequency (%)
241
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2394
88.6%
Common 309
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 314
13.1%
o 210
 
8.8%
n 181
 
7.6%
a 174
 
7.3%
t 166
 
6.9%
l 145
 
6.1%
g 140
 
5.8%
r 118
 
4.9%
i 110
 
4.6%
s 88
 
3.7%
Other values (39) 748
31.2%
Common
ValueCountFrequency (%)
241
78.0%
- 54
 
17.5%
? 4
 
1.3%
) 2
 
0.6%
( 2
 
0.6%
1 2
 
0.6%
3 2
 
0.6%
2 1
 
0.3%
' 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 314
 
11.6%
241
 
8.9%
o 210
 
7.8%
n 181
 
6.7%
a 174
 
6.4%
t 166
 
6.1%
l 145
 
5.4%
g 140
 
5.2%
r 118
 
4.4%
i 110
 
4.1%
Other values (48) 904
33.4%

행정 시
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Seoul
134 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Seoul 134
100.0%

Length

2023-12-11T15:40:56.330074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:40:56.419681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
seoul 134
100.0%

행정 구
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Jongno-gu
22 
Jung-gu
20 
Yangcheon-gu
11 
Mapo-gu
7 
Gangnam-gu
7 
Other values (18)
67 

Length

Max length15
Median length12
Mean length9.8283582
Min length7

Unique

Unique4 ?
Unique (%)3.0%

Sample

1st rowJung-gu
2nd rowJongno-gu
3rd rowMapo-gu
4th rowSeodaemun-gu
5th rowJongno-gu

Common Values

ValueCountFrequency (%)
Jongno-gu 22
16.4%
Jung-gu 20
14.9%
Yangcheon-gu 11
 
8.2%
Mapo-gu 7
 
5.2%
Gangnam-gu 7
 
5.2%
Seocho-gu 7
 
5.2%
Yongsan-gu 6
 
4.5%
Seodaemun-gu 6
 
4.5%
Gangdong-gu 6
 
4.5%
Yeongdeungpo-gu 5
 
3.7%
Other values (13) 37
27.6%

Length

2023-12-11T15:40:56.538362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jongno-gu 22
16.4%
jung-gu 20
14.9%
yangcheon-gu 11
 
8.2%
mapo-gu 7
 
5.2%
gangnam-gu 7
 
5.2%
seocho-gu 7
 
5.2%
yongsan-gu 6
 
4.5%
seodaemun-gu 6
 
4.5%
gangdong-gu 6
 
4.5%
gwanak-gu 5
 
3.7%
Other values (13) 37
27.6%

행정 동
Text

Distinct72
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T15:40:56.784426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length12.61194
Min length8

Characters and Unicode

Total characters1690
Distinct characters44
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)29.1%

Sample

1st rowHwanghak-dong
2nd rowJongno1.2.3.4ga-dong
3rd rowDaheung-dong
4th rowSinchon-dong
5th rowGahoe-dong
ValueCountFrequency (%)
mok5-dong 7
 
5.2%
gwanghui-dong 5
 
3.7%
jongno1.2.3.4ga-dong 5
 
3.7%
samcheong-dong 4
 
3.0%
sinjeong6-dong 4
 
3.0%
sinchon-dong 4
 
3.0%
insu-dong 4
 
3.0%
pyeongchang-dong 4
 
3.0%
myeong-dong 4
 
3.0%
hwanghak-dong 3
 
2.2%
Other values (62) 90
67.2%
2023-12-11T15:40:57.191389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 293
17.3%
g 242
14.3%
o 240
14.2%
d 138
 
8.2%
- 134
 
7.9%
a 97
 
5.7%
e 66
 
3.9%
h 50
 
3.0%
u 35
 
2.1%
S 28
 
1.7%
Other values (34) 367
21.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1341
79.3%
Dash Punctuation 134
 
7.9%
Uppercase Letter 134
 
7.9%
Decimal Number 65
 
3.8%
Other Punctuation 16
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 293
21.8%
g 242
18.0%
o 240
17.9%
d 138
10.3%
a 97
 
7.2%
e 66
 
4.9%
h 50
 
3.7%
u 35
 
2.6%
i 27
 
2.0%
y 27
 
2.0%
Other values (11) 126
9.4%
Uppercase Letter
ValueCountFrequency (%)
S 28
20.9%
J 15
11.2%
M 14
10.4%
G 12
9.0%
H 11
 
8.2%
I 9
 
6.7%
C 9
 
6.7%
B 7
 
5.2%
A 6
 
4.5%
Y 6
 
4.5%
Other values (5) 17
12.7%
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%
Dash Punctuation
ValueCountFrequency (%)
- 134
100.0%
Other Punctuation
ValueCountFrequency (%)
. 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1475
87.3%
Common 215
 
12.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 293
19.9%
g 242
16.4%
o 240
16.3%
d 138
9.4%
a 97
 
6.6%
e 66
 
4.5%
h 50
 
3.4%
u 35
 
2.4%
S 28
 
1.9%
i 27
 
1.8%
Other values (26) 259
17.6%
Common
ValueCountFrequency (%)
- 134
62.3%
2 19
 
8.8%
. 16
 
7.4%
1 13
 
6.0%
3 12
 
5.6%
5 8
 
3.7%
4 8
 
3.7%
6 5
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 293
17.3%
g 242
14.3%
o 240
14.2%
d 138
 
8.2%
- 134
 
7.9%
a 97
 
5.7%
e 66
 
3.9%
h 50
 
3.0%
u 35
 
2.1%
S 28
 
1.7%
Other values (34) 367
21.7%

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
2023-12-11T15:40:57.352725image/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
2023-12-11T15:40:57.491658image/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%
126.9803912024 4
 
3.0%
127.0045998616 4
 
3.0%
126.957402192 4
 
3.0%
126.9873802368 4
 
3.0%
126.8629865421 4
 
3.0%
126.9465464425 4
 
3.0%
126.9788194313 4
 
3.0%
127.0204336493 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
2023-12-11T15:40:57.637254image/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
2023-12-11T15:40:57.811194image/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%
37.5828099517 4
 
3.0%
37.6370942943 4
 
3.0%
37.6178956805 4
 
3.0%
37.5744028289 4
 
3.0%
37.516577197 4
 
3.0%
37.5611107256 4
 
3.0%
37.568058597 4
 
3.0%
37.568610138 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

2023-12-11T15:40:54.644403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:40:54.481958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:40:54.737263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:40:54.564245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:40:57.942489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정 구행정 동중심 좌표 X중심 좌표 Y
행정 구1.0001.0000.9530.918
행정 동1.0001.0001.0001.000
중심 좌표 X0.9531.0001.0000.627
중심 좌표 Y0.9181.0000.6271.000
2023-12-11T15:40:58.071371image/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

2023-12-11T15:40:54.837707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:40:54.948338image/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

키표기용 이름행정 시행정 구행정 동중심 좌표 X중심 좌표 Y
0BE_IW01-0085market housing developmentSeoulJung-guHwanghak-dong127.02043437.56861
1BE_IW01-0086Insadong Culture PlazaSeoulJongno-guJongno1.2.3.4ga-dong126.9873837.574403
2BE_IW01-0087Hongik Univ Street of ArtSeoulMapo-guDaheung-dong126.93792837.554241
3BE_IW01-0088Sinchon Ewha StreetSeoulSeodaemun-guSinchon-dong126.94654637.561111
4BE_IW01-0089Gyedong-gilSeoulJongno-guGahoe-dong126.98480737.582612
5BE_IW01-0090Gwanak-gu design streetSeoulGwanak-guJungang-dong126.94898637.482941
6BE_IW01-0091Geondae taste StreetSeoulGwangjin-guHwayang-dong127.07273637.543322
7BE_IW01-0092Hwarangno fallen leaves streetSeoulNowon-guGongneung2-dong127.08769337.630593
8BE_IW01-0093Rodeo StreetSeoulSeongdong-guYongdap-dong127.06129337.553694
9BE_IW01-0094Teenager Culture StreetSeoulYangcheon-guMok5-dong126.87589437.536597
키표기용 이름행정 시행정 구행정 동중심 좌표 X중심 좌표 Y
124BE_IW01-0075Byeolgung-gilSeoulJongno-guSamcheong-dong126.98039137.58281
125BE_IW01-0076Nonhyeondong cart bar AlleySeoulGangnam-guNonhyeon2-dong127.03119637.513636
126BE_IW01-0077The Philippines StreetSeoulJongno-guJongno1.2.3.4ga-dong126.9929937.58199
127BE_IW01-0078Gongdeok-dong Pig's feet AlleySeoulMapo-guAhyeon-dong126.95608937.554602
128BE_IW01-0079Cheonho-dongSeoulGangdong-guCheonho2-dong127.12667937.543179
129BE_IW01-0080Comme des Garoons steetSeoulYongsan-guHannam-dong127.00592937.535397
130BE_IW01-0081Baked fish AlleySeoulJongno-guJongno5.6ga-dong127.0001937.57369
131BE_IW01-0082ChinatownSeoulYeongdeungpo-guDaerim3-dong126.90127737.49675
132BE_IW01-0083ChinatownSeoulGuro-guGaribong-dong126.88730737.482833
133BE_IW01-0084ChinatownSeoulGwangjin-guJayang1-dong127.08021537.531921