Overview

Dataset statistics

Number of variables5
Number of observations115
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory42.1 B

Variable types

Categorical1
Numeric1
Text3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21285/A/1/datasetView.do

Alerts

NO is highly overall correlated with CATEGORYHigh correlation
CATEGORY is highly overall correlated with NOHigh correlation
NO has unique valuesUnique
AREA_CD has unique valuesUnique
AREA_NM has unique valuesUnique
ENG_NM has unique valuesUnique

Reproduction

Analysis started2024-05-04 00:17:25.099967
Analysis finished2024-05-04 00:17:26.877988
Duration1.78 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

CATEGORY
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
인구밀집지역
44 
발달상권
30 
공원
29 
관광특구
7 
고궁·문화유산
5 

Length

Max length7
Median length6
Mean length4.3913043
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인구밀집지역 44
38.3%
발달상권 30
26.1%
공원 29
25.2%
관광특구 7
 
6.1%
고궁·문화유산 5
 
4.3%

Length

2024-05-04T00:17:27.177148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:17:27.657752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인구밀집지역 44
38.3%
발달상권 30
26.1%
공원 29
25.2%
관광특구 7
 
6.1%
고궁·문화유산 5
 
4.3%

NO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct115
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58
Minimum1
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-04T00:17:28.272070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.7
Q129.5
median58
Q386.5
95-th percentile109.3
Maximum115
Range114
Interquartile range (IQR)57

Descriptive statistics

Standard deviation33.341666
Coefficient of variation (CV)0.5748563
Kurtosis-1.2
Mean58
Median Absolute Deviation (MAD)29
Skewness0
Sum6670
Variance1111.6667
MonotonicityStrictly increasing
2024-05-04T00:17:28.792476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.9%
74 1
 
0.9%
86 1
 
0.9%
85 1
 
0.9%
84 1
 
0.9%
83 1
 
0.9%
82 1
 
0.9%
81 1
 
0.9%
80 1
 
0.9%
79 1
 
0.9%
Other values (105) 105
91.3%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
115 1
0.9%
114 1
0.9%
113 1
0.9%
112 1
0.9%
111 1
0.9%
110 1
0.9%
109 1
0.9%
108 1
0.9%
107 1
0.9%
106 1
0.9%

AREA_CD
Text

UNIQUE 

Distinct115
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-05-04T00:17:29.789817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters690
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique115 ?
Unique (%)100.0%

Sample

1st rowPOI001
2nd rowPOI002
3rd rowPOI003
4th rowPOI004
5th rowPOI005
ValueCountFrequency (%)
poi001 1
 
0.9%
poi059 1
 
0.9%
poi085 1
 
0.9%
poi084 1
 
0.9%
poi083 1
 
0.9%
poi082 1
 
0.9%
poi081 1
 
0.9%
poi080 1
 
0.9%
poi079 1
 
0.9%
poi078 1
 
0.9%
Other values (105) 105
91.3%
2024-05-04T00:17:31.401662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 129
18.7%
P 115
16.7%
O 115
16.7%
I 115
16.7%
1 44
 
6.4%
3 22
 
3.2%
4 22
 
3.2%
5 22
 
3.2%
2 22
 
3.2%
6 21
 
3.0%
Other values (3) 63
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 345
50.0%
Uppercase Letter 345
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 129
37.4%
1 44
 
12.8%
3 22
 
6.4%
4 22
 
6.4%
5 22
 
6.4%
2 22
 
6.4%
6 21
 
6.1%
7 21
 
6.1%
8 21
 
6.1%
9 21
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
P 115
33.3%
O 115
33.3%
I 115
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 345
50.0%
Latin 345
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 129
37.4%
1 44
 
12.8%
3 22
 
6.4%
4 22
 
6.4%
5 22
 
6.4%
2 22
 
6.4%
6 21
 
6.1%
7 21
 
6.1%
8 21
 
6.1%
9 21
 
6.1%
Latin
ValueCountFrequency (%)
P 115
33.3%
O 115
33.3%
I 115
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 129
18.7%
P 115
16.7%
O 115
16.7%
I 115
16.7%
1 44
 
6.4%
3 22
 
3.2%
4 22
 
3.2%
5 22
 
3.2%
2 22
 
3.2%
6 21
 
3.0%
Other values (3) 63
9.1%

AREA_NM
Text

UNIQUE 

Distinct115
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-05-04T00:17:31.887675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length5.826087
Min length2

Characters and Unicode

Total characters670
Distinct characters191
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique115 ?
Unique (%)100.0%

Sample

1st row강남 MICE 관광특구
2nd row동대문 관광특구
3rd row명동 관광특구
4th row이태원 관광특구
5th row잠실 관광특구
ValueCountFrequency (%)
관광특구 7
 
5.1%
먹자골목 3
 
2.2%
이태원 2
 
1.4%
강남 1
 
0.7%
청담동 1
 
0.7%
앤틱가구거리 1
 
0.7%
인사동·익선동 1
 
0.7%
창동 1
 
0.7%
신경제 1
 
0.7%
중심지 1
 
0.7%
Other values (119) 119
86.2%
2024-05-04T00:17:32.841029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 47
 
7.0%
원 26
 
3.9%
23
 
3.4%
공 21
 
3.1%
대 18
 
2.7%
동 17
 
2.5%
구 17
 
2.5%
강 14
 
2.1%
· 14
 
2.1%
광 14
 
2.1%
Other values (181) 459
68.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 609
90.9%
Space Separator 23
 
3.4%
Other Punctuation 14
 
2.1%
Uppercase Letter 10
 
1.5%
Close Punctuation 5
 
0.7%
Open Punctuation 5
 
0.7%
Decimal Number 4
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 47
 
7.7%
원 26
 
4.3%
공 21
 
3.4%
대 18
 
3.0%
동 17
 
2.8%
구 17
 
2.8%
강 14
 
2.3%
광 14
 
2.3%
한 14
 
2.3%
리 13
 
2.1%
Other values (167) 408
67.0%
Uppercase Letter
ValueCountFrequency (%)
D 3
30.0%
M 2
20.0%
C 2
20.0%
P 1
 
10.0%
E 1
 
10.0%
I 1
 
10.0%
Decimal Number
ValueCountFrequency (%)
9 1
25.0%
1 1
25.0%
4 1
25.0%
2 1
25.0%
Space Separator
ValueCountFrequency (%)
23
100.0%
Other Punctuation
ValueCountFrequency (%)
· 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 609
90.9%
Common 51
 
7.6%
Latin 10
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 47
 
7.7%
원 26
 
4.3%
공 21
 
3.4%
대 18
 
3.0%
동 17
 
2.8%
구 17
 
2.8%
강 14
 
2.3%
광 14
 
2.3%
한 14
 
2.3%
리 13
 
2.1%
Other values (167) 408
67.0%
Common
ValueCountFrequency (%)
23
45.1%
· 14
27.5%
) 5
 
9.8%
( 5
 
9.8%
9 1
 
2.0%
1 1
 
2.0%
4 1
 
2.0%
2 1
 
2.0%
Latin
ValueCountFrequency (%)
D 3
30.0%
M 2
20.0%
C 2
20.0%
P 1
 
10.0%
E 1
 
10.0%
I 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 609
90.9%
ASCII 47
 
7.0%
None 14
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 47
 
7.7%
원 26
 
4.3%
공 21
 
3.4%
대 18
 
3.0%
동 17
 
2.8%
구 17
 
2.8%
강 14
 
2.3%
광 14
 
2.3%
한 14
 
2.3%
리 13
 
2.1%
Other values (167) 408
67.0%
ASCII
ValueCountFrequency (%)
23
48.9%
) 5
 
10.6%
( 5
 
10.6%
D 3
 
6.4%
M 2
 
4.3%
C 2
 
4.3%
P 1
 
2.1%
9 1
 
2.1%
1 1
 
2.1%
4 1
 
2.1%
Other values (3) 3
 
6.4%
None
ValueCountFrequency (%)
· 14
100.0%

ENG_NM
Text

UNIQUE 

Distinct115
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-05-04T00:17:33.408874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length75
Median length35
Mean length21.582609
Min length7

Characters and Unicode

Total characters2482
Distinct characters61
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique115 ?
Unique (%)100.0%

Sample

1st rowGangnam MICE Special Tourist Zone
2nd rowDongdaemun Fashion Town Special Tourist Zone
3rd rowMyeong-dong Namdaemun Bukchang-dong Da-dong Mugyo-dong Special Tourist Zone
4th rowItaewon Special Tourist Zone
5th rowJamsil Special Tourist Zone
ValueCountFrequency (%)
station 43
 
13.6%
park 17
 
5.4%
hangang 11
 
3.5%
seoul 8
 
2.5%
special 7
 
2.2%
zone 7
 
2.2%
university 7
 
2.2%
tourist 6
 
1.9%
street 6
 
1.9%
market 4
 
1.3%
Other values (167) 201
63.4%
2024-05-04T00:17:34.495301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 255
 
10.3%
a 245
 
9.9%
o 207
 
8.3%
203
 
8.2%
t 154
 
6.2%
e 153
 
6.2%
i 153
 
6.2%
g 127
 
5.1%
s 115
 
4.6%
r 88
 
3.5%
Other values (51) 782
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1957
78.8%
Uppercase Letter 273
 
11.0%
Space Separator 203
 
8.2%
Dash Punctuation 17
 
0.7%
Other Punctuation 16
 
0.6%
Close Punctuation 6
 
0.2%
Open Punctuation 6
 
0.2%
Decimal Number 4
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 255
13.0%
a 245
12.5%
o 207
10.6%
t 154
7.9%
e 153
 
7.8%
i 153
 
7.8%
g 127
 
6.5%
s 115
 
5.9%
r 88
 
4.5%
u 77
 
3.9%
Other values (16) 383
19.6%
Uppercase Letter
ValueCountFrequency (%)
S 38
13.9%
P 27
 
9.9%
C 22
 
8.1%
G 20
 
7.3%
H 19
 
7.0%
D 17
 
6.2%
T 15
 
5.5%
M 14
 
5.1%
N 13
 
4.8%
Y 11
 
4.0%
Other values (14) 77
28.2%
Decimal Number
ValueCountFrequency (%)
2 1
25.0%
9 1
25.0%
1 1
25.0%
4 1
25.0%
Other Punctuation
ValueCountFrequency (%)
· 11
68.8%
& 3
 
18.8%
' 2
 
12.5%
Space Separator
ValueCountFrequency (%)
203
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2230
89.8%
Common 252
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 255
 
11.4%
a 245
 
11.0%
o 207
 
9.3%
t 154
 
6.9%
e 153
 
6.9%
i 153
 
6.9%
g 127
 
5.7%
s 115
 
5.2%
r 88
 
3.9%
u 77
 
3.5%
Other values (40) 656
29.4%
Common
ValueCountFrequency (%)
203
80.6%
- 17
 
6.7%
· 11
 
4.4%
) 6
 
2.4%
( 6
 
2.4%
& 3
 
1.2%
' 2
 
0.8%
2 1
 
0.4%
9 1
 
0.4%
1 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2471
99.6%
None 11
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 255
 
10.3%
a 245
 
9.9%
o 207
 
8.4%
203
 
8.2%
t 154
 
6.2%
e 153
 
6.2%
i 153
 
6.2%
g 127
 
5.1%
s 115
 
4.7%
r 88
 
3.6%
Other values (50) 771
31.2%
None
ValueCountFrequency (%)
· 11
100.0%

Interactions

2024-05-04T00:17:25.685712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T00:17:34.766599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CATEGORYNO
CATEGORY1.0000.978
NO0.9781.000
2024-05-04T00:17:34.970119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NOCATEGORY
NO1.0000.771
CATEGORY0.7711.000

Missing values

2024-05-04T00:17:26.139828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T00:17:26.742662image/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

CATEGORYNOAREA_CDAREA_NMENG_NM
0관광특구1POI001강남 MICE 관광특구Gangnam MICE Special Tourist Zone
1관광특구2POI002동대문 관광특구Dongdaemun Fashion Town Special Tourist Zone
2관광특구3POI003명동 관광특구Myeong-dong Namdaemun Bukchang-dong Da-dong Mugyo-dong Special Tourist Zone
3관광특구4POI004이태원 관광특구Itaewon Special Tourist Zone
4관광특구5POI005잠실 관광특구Jamsil Special Tourist Zone
5관광특구6POI006종로·청계 관광특구Jongno Cheonggye Special Toruist Zone
6관광특구7POI007홍대 관광특구HongDae Culture & Arts Special Tourist Zone
7고궁·문화유산8POI008경복궁Gyeongbokgung Palace
8고궁·문화유산9POI009광화문·덕수궁Gwanghwamun & Deoksugung Palace
9고궁·문화유산10POI010보신각Bosingak
CATEGORYNOAREA_CDAREA_NMENG_NM
105공원106POI106월드컵공원World Cup Park
106공원107POI107응봉산Eungbongsan
107공원108POI108이촌한강공원Ichon Hangang Park
108공원109POI109잠실종합운동장Jamsil (Seoul) Sports Complex
109공원110POI110잠실한강공원Jamsil Hangang Park
110공원111POI111잠원한강공원Jamwon Hangang Park
111공원112POI112청계산Cheonggyesan
112공원113POI113청와대Cheongwadae
113발달상권114POI114북창동 먹자골목Bukchang-dong food alley
114발달상권115POI115남대문시장Namdaemun Market