Overview

Dataset statistics

Number of variables3
Number of observations146
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory24.9 B

Variable types

Categorical1
Text2

Dataset

Description인천광역시 부평구 출입기자 현황 데이터입니다.
Author인천광역시 부평구
URLhttps://www.data.go.kr/data/15088295/fileData.do

Alerts

출입기자 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:10:22.961845
Analysis finished2023-12-12 23:10:23.220547
Duration0.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

유형
Categorical

Distinct7
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
인터넷
75 
지역일간
29 
전국일간
16 
통신사
11 
방송사
9 
Other values (2)
 
6

Length

Max length4
Median length3
Mean length3.3493151
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인터넷 75
51.4%
지역일간 29
 
19.9%
전국일간 16
 
11.0%
통신사 11
 
7.5%
방송사 9
 
6.2%
지역주간 3
 
2.1%
전국주간 3
 
2.1%

Length

2023-12-13T08:10:23.272005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:10:23.367551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인터넷 75
51.4%
지역일간 29
 
19.9%
전국일간 16
 
11.0%
통신사 11
 
7.5%
방송사 9
 
6.2%
지역주간 3
 
2.1%
전국주간 3
 
2.1%

언론사
Text

Distinct137
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T08:10:23.600169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length10
Mean length5.4931507
Min length3

Characters and Unicode

Total characters802
Distinct characters162
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)88.4%

Sample

1st row중앙신문
2nd row경기도민일보
3rd row경기매일
4th row경기신문
5th row경도신문
ValueCountFrequency (%)
lg헬로비전 3
 
1.9%
tv방송 3
 
1.9%
북인천방송 3
 
1.9%
매일일보 2
 
1.3%
매일경제tv 2
 
1.3%
인천in 2
 
1.3%
뉴스피크 2
 
1.3%
미디어인천신문 2
 
1.3%
내외통신 2
 
1.3%
경기일보 2
 
1.3%
Other values (135) 135
85.4%
2023-12-13T08:10:23.969200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 52
 
6.5%
일 45
 
5.6%
뉴 43
 
5.4%
경 32
 
4.0%
보 31
 
3.9%
신 30
 
3.7%
이 24
 
3.0%
인 23
 
2.9%
문 23
 
2.9%
방 14
 
1.7%
Other values (152) 485
60.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 714
89.0%
Uppercase Letter 42
 
5.2%
Lowercase Letter 15
 
1.9%
Space Separator 12
 
1.5%
Decimal Number 8
 
1.0%
Close Punctuation 5
 
0.6%
Open Punctuation 5
 
0.6%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 52
 
7.3%
일 45
 
6.3%
뉴 43
 
6.0%
경 32
 
4.5%
보 31
 
4.3%
신 30
 
4.2%
이 24
 
3.4%
인 23
 
3.2%
문 23
 
3.2%
방 14
 
2.0%
Other values (123) 397
55.6%
Uppercase Letter
ValueCountFrequency (%)
T 12
28.6%
V 9
21.4%
S 3
 
7.1%
B 3
 
7.1%
G 3
 
7.1%
L 3
 
7.1%
N 2
 
4.8%
M 2
 
4.8%
C 1
 
2.4%
O 1
 
2.4%
Other values (3) 3
 
7.1%
Lowercase Letter
ValueCountFrequency (%)
e 4
26.7%
i 3
20.0%
n 3
20.0%
w 1
 
6.7%
s 1
 
6.7%
b 1
 
6.7%
u 1
 
6.7%
k 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
1 3
37.5%
2 3
37.5%
4 1
 
12.5%
5 1
 
12.5%
Space Separator
ValueCountFrequency (%)
12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 714
89.0%
Latin 57
 
7.1%
Common 31
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 52
 
7.3%
일 45
 
6.3%
뉴 43
 
6.0%
경 32
 
4.5%
보 31
 
4.3%
신 30
 
4.2%
이 24
 
3.4%
인 23
 
3.2%
문 23
 
3.2%
방 14
 
2.0%
Other values (123) 397
55.6%
Latin
ValueCountFrequency (%)
T 12
21.1%
V 9
15.8%
e 4
 
7.0%
S 3
 
5.3%
B 3
 
5.3%
G 3
 
5.3%
L 3
 
5.3%
i 3
 
5.3%
n 3
 
5.3%
N 2
 
3.5%
Other values (11) 12
21.1%
Common
ValueCountFrequency (%)
12
38.7%
) 5
16.1%
( 5
16.1%
1 3
 
9.7%
2 3
 
9.7%
- 1
 
3.2%
4 1
 
3.2%
5 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 714
89.0%
ASCII 88
 
11.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 52
 
7.3%
일 45
 
6.3%
뉴 43
 
6.0%
경 32
 
4.5%
보 31
 
4.3%
신 30
 
4.2%
이 24
 
3.4%
인 23
 
3.2%
문 23
 
3.2%
방 14
 
2.0%
Other values (123) 397
55.6%
ASCII
ValueCountFrequency (%)
12
13.6%
T 12
13.6%
V 9
 
10.2%
) 5
 
5.7%
( 5
 
5.7%
e 4
 
4.5%
S 3
 
3.4%
B 3
 
3.4%
G 3
 
3.4%
L 3
 
3.4%
Other values (19) 29
33.0%

출입기자
Text

UNIQUE 

Distinct146
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T08:10:24.231907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.2328767
Min length4

Characters and Unicode

Total characters764
Distinct characters128
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique146 ?
Unique (%)100.0%

Sample

1st row이복수국장
2nd row이원영부국장
3rd row김민립부장
4th row남용우사장
5th row김광수국장
ValueCountFrequency (%)
이복수국장 1
 
0.7%
조동옥본부장 1
 
0.7%
이장열편집장 1
 
0.7%
신성복국장 1
 
0.7%
조동환본부장 1
 
0.7%
임택국장 1
 
0.7%
윤효정국장 1
 
0.7%
정명달본부장 1
 
0.7%
김양훈부장 1
 
0.7%
김학철부장 1
 
0.7%
Other values (136) 136
93.2%
2023-12-13T08:10:24.621780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 92
 
12.0%
기 53
 
6.9%
자 51
 
6.7%
부 47
 
6.2%
국 37
 
4.8%
김 29
 
3.8%
본 24
 
3.1%
이 20
 
2.6%
영 16
 
2.1%
정 14
 
1.8%
Other values (118) 381
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 762
99.7%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 92
 
12.1%
기 53
 
7.0%
자 51
 
6.7%
부 47
 
6.2%
국 37
 
4.9%
김 29
 
3.8%
본 24
 
3.1%
이 20
 
2.6%
영 16
 
2.1%
정 14
 
1.8%
Other values (116) 379
49.7%
Uppercase Letter
ValueCountFrequency (%)
P 1
50.0%
D 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 762
99.7%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 92
 
12.1%
기 53
 
7.0%
자 51
 
6.7%
부 47
 
6.2%
국 37
 
4.9%
김 29
 
3.8%
본 24
 
3.1%
이 20
 
2.6%
영 16
 
2.1%
정 14
 
1.8%
Other values (116) 379
49.7%
Latin
ValueCountFrequency (%)
P 1
50.0%
D 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 762
99.7%
ASCII 2
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 92
 
12.1%
기 53
 
7.0%
자 51
 
6.7%
부 47
 
6.2%
국 37
 
4.9%
김 29
 
3.8%
본 24
 
3.1%
이 20
 
2.6%
영 16
 
2.1%
정 14
 
1.8%
Other values (116) 379
49.7%
ASCII
ValueCountFrequency (%)
P 1
50.0%
D 1
50.0%

Missing values

2023-12-13T08:10:23.134650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:10:23.197916image/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

유형언론사출입기자
0지역일간중앙신문이복수국장
1지역일간경기도민일보이원영부국장
2지역일간경기매일김민립부장
3지역일간경기신문남용우사장
4지역일간경도신문김광수국장
5지역일간경인종합일보김철국장
6지역일간내외일보김상규부장
7지역일간서울매일정순학본부장
8지역일간수도권일보윤명록본부장
9지역일간수도일보김광수본부장
유형언론사출입기자
136통신사중부뉴스통신김만식기자
137방송사iFM 경인방송강명윤기자
138방송사LG헬로비전 북인천방송 (TV방송)신창우PD
139방송사LG헬로비전 북인천방송 (TV방송)이재필기자
140방송사LG헬로비전 북인천방송 (TV방송)이정하기자
141방송사OBS(TV 방송)정보윤기자
142방송사TBN 경인교통방송이용일기자
143방송사TBS김호정기자
144방송사매일경제TV임덕철기자
145방송사매일경제TV백소민기자