Overview

Dataset statistics

Number of variables9
Number of observations31
Missing cells82
Missing cells (%)29.4%
Duplicate rows1
Duplicate rows (%)3.2%
Total size in memory2.3 KiB
Average record size in memory76.3 B

Variable types

Text6
Categorical2
Unsupported1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21778/F/1/datasetView.do

Alerts

Dataset has 1 (3.2%) duplicate rowsDuplicates
citydata/roadTraffic has 16 (51.6%) missing valuesMissing
Unnamed: 1 has 27 (87.1%) missing valuesMissing
Unnamed: 2 has 19 (61.3%) missing valuesMissing
Unnamed: 3 has 4 (12.9%) missing valuesMissing
Unnamed: 6 has 4 (12.9%) missing valuesMissing
Unnamed: 7 has 5 (16.1%) missing valuesMissing
Unnamed: 8 has 7 (22.6%) missing valuesMissing
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-13 13:13:34.693668
Analysis finished2024-03-13 13:13:35.530341
Duration0.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

citydata/roadTraffic
Text

MISSING 

Distinct13
Distinct (%)86.7%
Missing16
Missing (%)51.6%
Memory size380.0 B
2024-03-13T22:13:35.671794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length10.866667
Min length4

Characters and Unicode

Total characters163
Distinct characters59
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)73.3%

Sample

1st row요청 메시지 명세
2nd row항목명(영문)
3rd rowServiceKey
4th rowhotspotNm
5th rowhotspotId
ValueCountFrequency (%)
항목명(영문 2
 
8.0%
항목구분 2
 
8.0%
필수(1 2
 
8.0%
옵션(0 2
 
8.0%
메시지 2
 
8.0%
명세 2
 
8.0%
※ 2
 
8.0%
응답 1
 
4.0%
area_nm 1
 
4.0%
list_total_count 1
 
4.0%
Other values (8) 8
32.0%
2024-03-13T22:13:36.019489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 11
 
6.7%
10
 
6.1%
e 7
 
4.3%
o 7
 
4.3%
( 6
 
3.7%
) 6
 
3.7%
s 6
 
3.7%
_ 5
 
3.1%
l 4
 
2.5%
목 4
 
2.5%
Other values (49) 97
59.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 59
36.2%
Other Letter 40
24.5%
Uppercase Letter 27
16.6%
Space Separator 10
 
6.1%
Open Punctuation 6
 
3.7%
Close Punctuation 6
 
3.7%
Other Punctuation 6
 
3.7%
Connector Punctuation 5
 
3.1%
Decimal Number 4
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
목 4
 
10.0%
항 4
 
10.0%
명 4
 
10.0%
분 2
 
5.0%
필 2
 
5.0%
수 2
 
5.0%
세 2
 
5.0%
문 2
 
5.0%
구 2
 
5.0%
옵 2
 
5.0%
Other values (9) 14
35.0%
Lowercase Letter
ValueCountFrequency (%)
t 11
18.6%
e 7
11.9%
o 7
11.9%
s 6
10.2%
l 4
 
6.8%
r 3
 
5.1%
p 3
 
5.1%
u 3
 
5.1%
d 2
 
3.4%
h 2
 
3.4%
Other values (8) 11
18.6%
Uppercase Letter
ValueCountFrequency (%)
A 4
14.8%
R 3
11.1%
T 3
11.1%
S 3
11.1%
C 2
7.4%
M 2
7.4%
I 2
7.4%
N 2
7.4%
F 2
7.4%
O 1
 
3.7%
Other values (3) 3
11.1%
Other Punctuation
ValueCountFrequency (%)
, 2
33.3%
※ 2
33.3%
: 2
33.3%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 86
52.8%
Hangul 40
24.5%
Common 37
22.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 11
 
12.8%
e 7
 
8.1%
o 7
 
8.1%
s 6
 
7.0%
l 4
 
4.7%
A 4
 
4.7%
r 3
 
3.5%
p 3
 
3.5%
R 3
 
3.5%
T 3
 
3.5%
Other values (21) 35
40.7%
Hangul
ValueCountFrequency (%)
목 4
 
10.0%
항 4
 
10.0%
명 4
 
10.0%
분 2
 
5.0%
필 2
 
5.0%
수 2
 
5.0%
세 2
 
5.0%
문 2
 
5.0%
구 2
 
5.0%
옵 2
 
5.0%
Other values (9) 14
35.0%
Common
ValueCountFrequency (%)
10
27.0%
( 6
16.2%
) 6
16.2%
_ 5
13.5%
, 2
 
5.4%
※ 2
 
5.4%
: 2
 
5.4%
1 2
 
5.4%
0 2
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
74.2%
Hangul 40
 
24.5%
Punctuation 2
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 11
 
9.1%
10
 
8.3%
e 7
 
5.8%
o 7
 
5.8%
( 6
 
5.0%
) 6
 
5.0%
s 6
 
5.0%
_ 5
 
4.1%
l 4
 
3.3%
A 4
 
3.3%
Other values (29) 55
45.5%
Hangul
ValueCountFrequency (%)
목 4
 
10.0%
항 4
 
10.0%
명 4
 
10.0%
분 2
 
5.0%
필 2
 
5.0%
수 2
 
5.0%
세 2
 
5.0%
문 2
 
5.0%
구 2
 
5.0%
옵 2
 
5.0%
Other values (9) 14
35.0%
Punctuation
ValueCountFrequency (%)
※ 2
100.0%

Unnamed: 1
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing27
Missing (%)87.1%
Memory size380.0 B
2024-03-13T22:13:36.172714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16.5
Mean length14.25
Min length8

Characters and Unicode

Total characters57
Distinct characters15
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

Unique4 ?
Unique (%)100.0%

Sample

1st rowROAD_TRAFFIC_SPD
2nd rowROAD_TRAFFIC_IDX
3rd rowROAD_TRAFFIC_TIME
4th rowROAD_MSG
ValueCountFrequency (%)
road_traffic_spd 1
25.0%
road_traffic_idx 1
25.0%
road_traffic_time 1
25.0%
road_msg 1
25.0%
2024-03-13T22:13:36.461311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 7
12.3%
A 7
12.3%
_ 7
12.3%
D 6
10.5%
F 6
10.5%
I 5
8.8%
O 4
7.0%
T 4
7.0%
C 3
5.3%
S 2
 
3.5%
Other values (5) 6
10.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 50
87.7%
Connector Punctuation 7
 
12.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 7
14.0%
A 7
14.0%
D 6
12.0%
F 6
12.0%
I 5
10.0%
O 4
8.0%
T 4
8.0%
C 3
6.0%
S 2
 
4.0%
M 2
 
4.0%
Other values (4) 4
8.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50
87.7%
Common 7
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 7
14.0%
A 7
14.0%
D 6
12.0%
F 6
12.0%
I 5
10.0%
O 4
8.0%
T 4
8.0%
C 3
6.0%
S 2
 
4.0%
M 2
 
4.0%
Other values (4) 4
8.0%
Common
ValueCountFrequency (%)
_ 7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 7
12.3%
A 7
12.3%
_ 7
12.3%
D 6
10.5%
F 6
10.5%
I 5
8.8%
O 4
7.0%
T 4
7.0%
C 3
5.3%
S 2
 
3.5%
Other values (5) 6
10.5%

Unnamed: 2
Text

MISSING 

Distinct12
Distinct (%)100.0%
Missing19
Missing (%)61.3%
Memory size380.0 B
2024-03-13T22:13:36.628248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length7.5
Min length3

Characters and Unicode

Total characters90
Distinct characters17
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

Unique12 ?
Unique (%)100.0%

Sample

1st rowLINK_ID
2nd rowROAD_NM
3rd rowSTART_ND_CD
4th rowSTART_ND_NM
5th rowSTART_ND_XY
ValueCountFrequency (%)
link_id 1
8.3%
road_nm 1
8.3%
start_nd_cd 1
8.3%
start_nd_nm 1
8.3%
start_nd_xy 1
8.3%
end_nd_cd 1
8.3%
end_nd_nm 1
8.3%
end_nd_xy 1
8.3%
dist 1
8.3%
spd 1
8.3%
Other values (2) 2
16.7%
2024-03-13T22:13:36.946216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 16
17.8%
_ 14
15.6%
N 13
14.4%
T 8
8.9%
S 6
 
6.7%
I 5
 
5.6%
A 4
 
4.4%
X 4
 
4.4%
R 4
 
4.4%
E 3
 
3.3%
Other values (7) 13
14.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 76
84.4%
Connector Punctuation 14
 
15.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 16
21.1%
N 13
17.1%
T 8
10.5%
S 6
 
7.9%
I 5
 
6.6%
A 4
 
5.3%
X 4
 
5.3%
R 4
 
5.3%
E 3
 
3.9%
Y 3
 
3.9%
Other values (6) 10
13.2%
Connector Punctuation
ValueCountFrequency (%)
_ 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 76
84.4%
Common 14
 
15.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 16
21.1%
N 13
17.1%
T 8
10.5%
S 6
 
7.9%
I 5
 
6.6%
A 4
 
5.3%
X 4
 
5.3%
R 4
 
5.3%
E 3
 
3.9%
Y 3
 
3.9%
Other values (6) 10
13.2%
Common
ValueCountFrequency (%)
_ 14
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 16
17.8%
_ 14
15.6%
N 13
14.4%
T 8
8.9%
S 6
 
6.7%
I 5
 
5.6%
A 4
 
4.4%
X 4
 
4.4%
R 4
 
4.4%
E 3
 
3.3%
Other values (7) 13
14.4%

Unnamed: 3
Text

MISSING 

Distinct26
Distinct (%)96.3%
Missing4
Missing (%)12.9%
Memory size380.0 B
2024-03-13T22:13:37.163724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length7.962963
Min length3

Characters and Unicode

Total characters215
Distinct characters67
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)92.6%

Sample

1st row항목명(국문)
2nd row서비스키
3rd row주요구역명
4th row주요구역 코드
5th row반환 타입
ValueCountFrequency (%)
코드 3
 
7.9%
항목명(국문 2
 
5.3%
도로소통현황 2
 
5.3%
전체도로소통평균현황 2
 
5.3%
서비스키 1
 
2.6%
id 1
 
2.6%
링크아이디 1
 
2.6%
메세지 1
 
2.6%
도로구간소통지표 1
 
2.6%
도로구간평균속도 1
 
2.6%
Other values (23) 23
60.5%
2024-03-13T22:13:37.480211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 18
 
8.4%
로 16
 
7.4%
11
 
5.1%
드 10
 
4.7%
명 7
 
3.3%
지 7
 
3.3%
소 7
 
3.3%
노 6
 
2.8%
간 6
 
2.8%
통 6
 
2.8%
Other values (57) 121
56.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 192
89.3%
Space Separator 11
 
5.1%
Uppercase Letter 6
 
2.8%
Close Punctuation 3
 
1.4%
Open Punctuation 3
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 18
 
9.4%
로 16
 
8.3%
드 10
 
5.2%
명 7
 
3.6%
지 7
 
3.6%
소 7
 
3.6%
노 6
 
3.1%
간 6
 
3.1%
통 6
 
3.1%
구 6
 
3.1%
Other values (49) 103
53.6%
Uppercase Letter
ValueCountFrequency (%)
I 2
33.3%
N 1
16.7%
D 1
16.7%
K 1
16.7%
L 1
16.7%
Space Separator
ValueCountFrequency (%)
11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 192
89.3%
Common 17
 
7.9%
Latin 6
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 18
 
9.4%
로 16
 
8.3%
드 10
 
5.2%
명 7
 
3.6%
지 7
 
3.6%
소 7
 
3.6%
노 6
 
3.1%
간 6
 
3.1%
통 6
 
3.1%
구 6
 
3.1%
Other values (49) 103
53.6%
Latin
ValueCountFrequency (%)
I 2
33.3%
N 1
16.7%
D 1
16.7%
K 1
16.7%
L 1
16.7%
Common
ValueCountFrequency (%)
11
64.7%
) 3
 
17.6%
( 3
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 192
89.3%
ASCII 23
 
10.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 18
 
9.4%
로 16
 
8.3%
드 10
 
5.2%
명 7
 
3.6%
지 7
 
3.6%
소 7
 
3.6%
노 6
 
3.1%
간 6
 
3.1%
통 6
 
3.1%
구 6
 
3.1%
Other values (49) 103
53.6%
ASCII
ValueCountFrequency (%)
11
47.8%
) 3
 
13.0%
( 3
 
13.0%
I 2
 
8.7%
N 1
 
4.3%
D 1
 
4.3%
K 1
 
4.3%
L 1
 
4.3%

Unnamed: 4
Categorical

Distinct7
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Memory size380.0 B
ITS 교통소통정보
17 
<NA>
9 
열광에서 관리하는 파라미터
 
1
주요구역 명 대체 예정
 
1
신규 추가
 
1
Other values (2)
2 

Length

Max length14
Median length10
Mean length8.2580645
Min length4

Unique

Unique5 ?
Unique (%)16.1%

Sample

1st row<NA>
2nd row<NA>
3rd row열광에서 관리하는 파라미터
4th row<NA>
5th row주요구역 명 대체 예정

Common Values

ValueCountFrequency (%)
ITS 교통소통정보 17
54.8%
<NA> 9
29.0%
열광에서 관리하는 파라미터 1
 
3.2%
주요구역 명 대체 예정 1
 
3.2%
신규 추가 1
 
3.2%
데이터 연계 API 1
 
3.2%
KT 실시간 인구 1
 
3.2%

Length

2024-03-13T22:13:37.616377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:13:37.749793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
its 17
29.3%
교통소통정보 17
29.3%
na 9
15.5%
신규 1
 
1.7%
실시간 1
 
1.7%
kt 1
 
1.7%
api 1
 
1.7%
연계 1
 
1.7%
데이터 1
 
1.7%
추가 1
 
1.7%
Other values (8) 8
13.8%

Unnamed: 5
Categorical

Distinct5
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size380.0 B
String
19 
<NA>
7 
데이터타입
2 
Double
2 
Object
 
1

Length

Max length6
Median length6
Mean length5.483871
Min length4

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row<NA>
2nd row데이터타입
3rd rowString
4th rowString
5th rowString

Common Values

ValueCountFrequency (%)
String 19
61.3%
<NA> 7
 
22.6%
데이터타입 2
 
6.5%
Double 2
 
6.5%
Object 1
 
3.2%

Length

2024-03-13T22:13:37.911552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:13:38.026589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
string 19
61.3%
na 7
 
22.6%
데이터타입 2
 
6.5%
double 2
 
6.5%
object 1
 
3.2%

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)12.9%
Memory size380.0 B

Unnamed: 7
Text

MISSING 

Distinct22
Distinct (%)84.6%
Missing5
Missing (%)16.1%
Memory size380.0 B
2024-03-13T22:13:38.207334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length163
Median length14.5
Mean length16.346154
Min length1

Characters and Unicode

Total characters425
Distinct characters86
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)69.2%

Sample

1st row샘플데이터
2nd row서비스키
3rd rowDMC(디지털미디어시티)
4th rowPOI0001
5th rowjson/xml
ValueCountFrequency (%)
15 2
 
5.1%
샘플데이터 2
 
5.1%
dmc(디지털미디어시티 2
 
5.1%
서행 2
 
5.1%
있어요 2
 
5.1%
16:00 1
 
2.6%
소요될 1
 
2.6%
수 1
 
2.6%
잠실대교북단 1
 
2.6%
2022-08-05 1
 
2.6%
Other values (24) 24
61.5%
2024-03-13T22:13:38.599257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 32
 
7.5%
2 31
 
7.3%
3 31
 
7.3%
0 29
 
6.8%
1 29
 
6.8%
8 28
 
6.6%
5 25
 
5.9%
9 23
 
5.4%
7 23
 
5.4%
4 19
 
4.5%
Other values (76) 155
36.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
63.5%
Other Letter 83
 
19.5%
Other Punctuation 17
 
4.0%
Lowercase Letter 17
 
4.0%
Space Separator 13
 
3.1%
Uppercase Letter 9
 
2.1%
Connector Punctuation 7
 
1.6%
Math Symbol 3
 
0.7%
Close Punctuation 2
 
0.5%
Dash Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 4
 
4.8%
디 4
 
4.8%
어 4
 
4.8%
시 4
 
4.8%
실 3
 
3.6%
요 3
 
3.6%
잠 3
 
3.6%
서 3
 
3.6%
교 2
 
2.4%
지 2
 
2.4%
Other values (38) 51
61.4%
Lowercase Letter
ValueCountFrequency (%)
o 3
17.6%
m 2
11.8%
n 2
11.8%
l 2
11.8%
e 1
 
5.9%
x 1
 
5.9%
s 1
 
5.9%
j 1
 
5.9%
d 1
 
5.9%
c 1
 
5.9%
Other values (2) 2
11.8%
Decimal Number
ValueCountFrequency (%)
6 32
11.9%
2 31
11.5%
3 31
11.5%
0 29
10.7%
1 29
10.7%
8 28
10.4%
5 25
9.3%
9 23
8.5%
7 23
8.5%
4 19
7.0%
Uppercase Letter
ValueCountFrequency (%)
C 2
22.2%
M 2
22.2%
D 2
22.2%
I 1
11.1%
O 1
11.1%
P 1
11.1%
Other Punctuation
ValueCountFrequency (%)
. 14
82.4%
/ 1
 
5.9%
: 1
 
5.9%
, 1
 
5.9%
Space Separator
ValueCountFrequency (%)
13
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Math Symbol
ValueCountFrequency (%)
| 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 316
74.4%
Hangul 83
 
19.5%
Latin 26
 
6.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 4
 
4.8%
디 4
 
4.8%
어 4
 
4.8%
시 4
 
4.8%
실 3
 
3.6%
요 3
 
3.6%
잠 3
 
3.6%
서 3
 
3.6%
교 2
 
2.4%
지 2
 
2.4%
Other values (38) 51
61.4%
Common
ValueCountFrequency (%)
6 32
10.1%
2 31
9.8%
3 31
9.8%
0 29
9.2%
1 29
9.2%
8 28
8.9%
5 25
7.9%
9 23
7.3%
7 23
7.3%
4 19
6.0%
Other values (10) 46
14.6%
Latin
ValueCountFrequency (%)
o 3
 
11.5%
C 2
 
7.7%
M 2
 
7.7%
D 2
 
7.7%
m 2
 
7.7%
n 2
 
7.7%
l 2
 
7.7%
e 1
 
3.8%
x 1
 
3.8%
s 1
 
3.8%
Other values (8) 8
30.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 342
80.5%
Hangul 83
 
19.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 32
9.4%
2 31
9.1%
3 31
9.1%
0 29
8.5%
1 29
8.5%
8 28
 
8.2%
5 25
 
7.3%
9 23
 
6.7%
7 23
 
6.7%
4 19
 
5.6%
Other values (28) 72
21.1%
Hangul
ValueCountFrequency (%)
이 4
 
4.8%
디 4
 
4.8%
어 4
 
4.8%
시 4
 
4.8%
실 3
 
3.6%
요 3
 
3.6%
잠 3
 
3.6%
서 3
 
3.6%
교 2
 
2.4%
지 2
 
2.4%
Other values (38) 51
61.4%

Unnamed: 8
Text

MISSING 

Distinct23
Distinct (%)95.8%
Missing7
Missing (%)22.6%
Memory size380.0 B
2024-03-13T22:13:38.826288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length26
Mean length14.458333
Min length4

Characters and Unicode

Total characters347
Distinct characters100
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)91.7%

Sample

1st row항목설명
2nd row서비스키
3rd row주요구역명
4th row주요구역 고유코드 (샘플은 DMC(디지털미디어시티))
5th row반환타입 지정 파라미터
ValueCountFrequency (%)
도로 10
 
11.0%
구간 5
 
5.5%
조회한 5
 
5.5%
포함된 4
 
4.4%
평균 4
 
4.4%
소통 4
 
4.4%
내 4
 
4.4%
구역 4
 
4.4%
실시간 2
 
2.2%
도로의 2
 
2.2%
Other values (44) 47
51.6%
2024-03-13T22:13:39.172666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67
 
19.3%
도 19
 
5.5%
로 18
 
5.2%
구 14
 
4.0%
간 9
 
2.6%
한 8
 
2.3%
명 7
 
2.0%
시 7
 
2.0%
역 7
 
2.0%
( 6
 
1.7%
Other values (90) 185
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 256
73.8%
Space Separator 67
 
19.3%
Lowercase Letter 7
 
2.0%
Open Punctuation 6
 
1.7%
Close Punctuation 6
 
1.7%
Uppercase Letter 3
 
0.9%
Other Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 19
 
7.4%
로 18
 
7.0%
구 14
 
5.5%
간 9
 
3.5%
한 8
 
3.1%
명 7
 
2.7%
시 7
 
2.7%
역 7
 
2.7%
지 6
 
2.3%
소 6
 
2.3%
Other values (80) 155
60.5%
Lowercase Letter
ValueCountFrequency (%)
m 3
42.9%
k 2
28.6%
h 2
28.6%
Uppercase Letter
ValueCountFrequency (%)
D 1
33.3%
M 1
33.3%
C 1
33.3%
Space Separator
ValueCountFrequency (%)
67
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 256
73.8%
Common 81
 
23.3%
Latin 10
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 19
 
7.4%
로 18
 
7.0%
구 14
 
5.5%
간 9
 
3.5%
한 8
 
3.1%
명 7
 
2.7%
시 7
 
2.7%
역 7
 
2.7%
지 6
 
2.3%
소 6
 
2.3%
Other values (80) 155
60.5%
Latin
ValueCountFrequency (%)
m 3
30.0%
k 2
20.0%
h 2
20.0%
D 1
 
10.0%
M 1
 
10.0%
C 1
 
10.0%
Common
ValueCountFrequency (%)
67
82.7%
( 6
 
7.4%
) 6
 
7.4%
/ 2
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 256
73.8%
ASCII 91
 
26.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67
73.6%
( 6
 
6.6%
) 6
 
6.6%
m 3
 
3.3%
/ 2
 
2.2%
k 2
 
2.2%
h 2
 
2.2%
D 1
 
1.1%
M 1
 
1.1%
C 1
 
1.1%
Hangul
ValueCountFrequency (%)
도 19
 
7.4%
로 18
 
7.0%
구 14
 
5.5%
간 9
 
3.5%
한 8
 
3.1%
명 7
 
2.7%
시 7
 
2.7%
역 7
 
2.7%
지 6
 
2.3%
소 6
 
2.3%
Other values (80) 155
60.5%

Correlations

2024-03-13T22:13:39.269598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
citydata/roadTrafficUnnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 7Unnamed: 8
citydata/roadTraffic1.000NaNNaN1.0001.0001.0001.0001.000
Unnamed: 1NaN1.000NaN1.000NaN1.0001.0001.000
Unnamed: 2NaNNaN1.0001.000NaN1.0001.0001.000
Unnamed: 31.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 41.000NaNNaN1.0001.0000.5571.0001.000
Unnamed: 51.0001.0001.0001.0000.5571.0001.0001.000
Unnamed: 71.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 81.0001.0001.0001.0001.0001.0001.0001.000
2024-03-13T22:13:39.393444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 4Unnamed: 5
Unnamed: 41.0000.355
Unnamed: 50.3551.000
2024-03-13T22:13:39.481539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 4Unnamed: 5
Unnamed: 41.0000.355
Unnamed: 50.3551.000

Missing values

2024-03-13T22:13:35.119190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:13:35.252074image/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.
2024-03-13T22:13:35.401753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

citydata/roadTrafficUnnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
0요청 메시지 명세<NA><NA><NA><NA><NA>NaN<NA><NA>
1항목명(영문)<NA><NA>항목명(국문)<NA>데이터타입항목구분샘플데이터항목설명
2ServiceKey<NA><NA>서비스키열광에서 관리하는 파라미터String1서비스키서비스키
3hotspotNm<NA><NA>주요구역명<NA>String1DMC(디지털미디어시티)주요구역명
4hotspotId<NA><NA>주요구역 코드주요구역 명 대체 예정String1POI0001주요구역 고유코드 (샘플은 DMC(디지털미디어시티))
5type<NA><NA>반환 타입신규 추가String1json/xml반환타입 지정 파라미터
6※ 항목구분: 필수(1), 옵션(0)<NA><NA><NA><NA><NA>NaN<NA><NA>
7응답 메시지 명세<NA><NA><NA><NA><NA>NaN<NA><NA>
8항목명(영문)<NA><NA>항목명(국문)데이터 연계 API데이터타입항목구분샘플데이터항목설명
9resultCode<NA><NA>결과코드<NA><NA>10결과코드
citydata/roadTrafficUnnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
21<NA><NA>START_ND_NM도로노드시작명ITS 교통소통정보String1잠실대교북단도로 구간 시작명
22<NA><NA>START_ND_XY도로노드시작지점좌표ITS 교통소통정보String1126.9448026708143971_37.5338038169339328<NA>
23<NA><NA>END_ND_CD도로노드종료지점 코드ITS 교통소통정보String112551221도로 구간 종료지점 코드
24<NA><NA>END_ND_NM도로노드종료명ITS 교통소통정보String1잠실대교남단도로 구간 종료명
25<NA><NA>END_ND_XY도로노드종료지점좌표ITS 교통소통정보String1126.9439667948493735_37.5340787367079329<NA>
26<NA><NA>DIST도로구간길이ITS 교통소통정보String192도로 구간 길이(m)
27<NA><NA>SPD도로구간평균속도ITS 교통소통정보Double115도로 평균 속도(km/h)
28<NA><NA>IDX도로구간소통지표ITS 교통소통정보String1서행도로 소통 지표
29<NA><NA>XYLIST링크아이디 좌표(보간점)ITS 교통소통정보String1126.9895649833617739_37.5683143760920686|126.9885265106382235_37.5682545626432542|126.9880976408843054_37.5682583350004506|126.9876861987615371_37.5682795806278946<NA>
30※ 항목구분: 필수(1), 옵션(0)<NA><NA><NA><NA><NA>NaN<NA><NA>

Duplicate rows

Most frequently occurring

citydata/roadTrafficUnnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 7Unnamed: 8# duplicates
0※ 항목구분: 필수(1), 옵션(0)<NA><NA><NA><NA><NA><NA><NA>2