Overview

Dataset statistics

Number of variables4
Number of observations527
Missing cells387
Missing cells (%)18.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.1 KiB
Average record size in memory33.3 B

Variable types

Numeric1
Text3

Dataset

Description대구광역시 남구 자체 문자를 발송하는 알리미시스템의 코드에 대한 데이터로 주코드, 부코드, 코드명, 코드설명 등의 항목을 제공합니다.
Author대구광역시 남구
URLhttps://www.data.go.kr/data/15089399/fileData.do

Alerts

부코드 has 37 (7.0%) missing valuesMissing
코드설명 has 350 (66.4%) missing valuesMissing
주코드 has 39 (7.4%) zerosZeros

Reproduction

Analysis started2023-12-12 10:26:48.179876
Analysis finished2023-12-12 10:26:48.770126
Duration0.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

주코드
Real number (ℝ)

ZEROS 

Distinct36
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.98482
Minimum0
Maximum210
Zeros39
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-12-12T19:26:48.829762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median24
Q3102
95-th percentile107
Maximum210
Range210
Interquartile range (IQR)88

Descriptive statistics

Standard deviation45.858931
Coefficient of variation (CV)0.84947827
Kurtosis-1.7030688
Mean53.98482
Median Absolute Deviation (MAD)24
Skewness0.19142761
Sum28450
Variance2103.0416
MonotonicityIncreasing
2023-12-12T19:26:48.983667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
102 106
20.1%
18 95
18.0%
0 39
 
7.4%
101 37
 
7.0%
107 37
 
7.0%
104 37
 
7.0%
5 25
 
4.7%
103 15
 
2.8%
12 13
 
2.5%
6 13
 
2.5%
Other values (26) 110
20.9%
ValueCountFrequency (%)
0 39
7.4%
1 4
 
0.8%
2 5
 
0.9%
3 4
 
0.8%
4 7
 
1.3%
5 25
4.7%
6 13
 
2.5%
8 4
 
0.8%
9 3
 
0.6%
10 5
 
0.9%
ValueCountFrequency (%)
210 1
 
0.2%
109 1
 
0.2%
108 1
 
0.2%
107 37
 
7.0%
106 1
 
0.2%
105 3
 
0.6%
104 37
 
7.0%
103 15
 
2.8%
102 106
20.1%
101 37
 
7.0%

부코드
Text

MISSING 

Distinct331
Distinct (%)67.6%
Missing37
Missing (%)7.0%
Memory size4.2 KiB
2023-12-12T19:26:49.262312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.1959184
Min length1

Characters and Unicode

Total characters2056
Distinct characters54
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

Unique258 ?
Unique (%)52.7%

Sample

1st row1
2nd row3
3rd row4
4th row5
5th row7
ValueCountFrequency (%)
1 24
 
4.9%
2 18
 
3.7%
3 15
 
3.1%
4 9
 
1.8%
5 8
 
1.6%
0 8
 
1.6%
7 5
 
1.0%
10 5
 
1.0%
8 4
 
0.8%
9 4
 
0.8%
Other values (313) 390
79.6%
2023-12-12T19:26:49.929178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 528
25.7%
4 288
14.0%
3 224
10.9%
1 186
 
9.0%
2 150
 
7.3%
6 140
 
6.8%
9 87
 
4.2%
7 80
 
3.9%
5 60
 
2.9%
8 56
 
2.7%
Other values (44) 257
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1799
87.5%
Uppercase Letter 184
 
8.9%
Lowercase Letter 70
 
3.4%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10
14.3%
c 10
14.3%
t 10
14.3%
o 7
10.0%
j 7
10.0%
b 7
10.0%
z 1
 
1.4%
y 1
 
1.4%
x 1
 
1.4%
w 1
 
1.4%
Other values (15) 15
21.4%
Uppercase Letter
ValueCountFrequency (%)
N 37
20.1%
S 36
19.6%
M 24
13.0%
C 21
11.4%
A 14
 
7.6%
F 8
 
4.3%
P 6
 
3.3%
T 6
 
3.3%
E 6
 
3.3%
D 5
 
2.7%
Other values (8) 21
11.4%
Decimal Number
ValueCountFrequency (%)
0 528
29.3%
4 288
16.0%
3 224
12.5%
1 186
 
10.3%
2 150
 
8.3%
6 140
 
7.8%
9 87
 
4.8%
7 80
 
4.4%
5 60
 
3.3%
8 56
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1802
87.6%
Latin 254
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 37
14.6%
S 36
14.2%
M 24
 
9.4%
C 21
 
8.3%
A 14
 
5.5%
e 10
 
3.9%
c 10
 
3.9%
t 10
 
3.9%
F 8
 
3.1%
o 7
 
2.8%
Other values (33) 77
30.3%
Common
ValueCountFrequency (%)
0 528
29.3%
4 288
16.0%
3 224
12.4%
1 186
 
10.3%
2 150
 
8.3%
6 140
 
7.8%
9 87
 
4.8%
7 80
 
4.4%
5 60
 
3.3%
8 56
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 528
25.7%
4 288
14.0%
3 224
10.9%
1 186
 
9.0%
2 150
 
7.3%
6 140
 
6.8%
9 87
 
4.2%
7 80
 
3.9%
5 60
 
2.9%
8 56
 
2.7%
Other values (44) 257
12.5%

코드명
Text

Distinct387
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2023-12-12T19:26:50.298740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length29
Mean length7.5616698
Min length1

Characters and Unicode

Total characters3985
Distinct characters298
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

Unique336 ?
Unique (%)63.8%

Sample

1st row시스템
2nd row민원정보과
3rd row20111100000000
4th row20080100000000
5th row1
ValueCountFrequency (%)
true 23
 
3.1%
이통사 20
 
2.7%
메시지 13
 
1.8%
false 12
 
1.6%
단말기 10
 
1.4%
전송 8
 
1.1%
오류 7
 
1.0%
536642212 6
 
0.8%
536642514 6
 
0.8%
536642943 6
 
0.8%
Other values (455) 624
84.9%
2023-12-12T19:26:51.125407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 223
 
5.6%
210
 
5.3%
3 210
 
5.3%
5 163
 
4.1%
0 150
 
3.8%
4 149
 
3.7%
지 146
 
3.7%
1 111
 
2.8%
방 110
 
2.8%
2 107
 
2.7%
Other values (288) 2406
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1969
49.4%
Decimal Number 1175
29.5%
Uppercase Letter 341
 
8.6%
Lowercase Letter 211
 
5.3%
Space Separator 210
 
5.3%
Close Punctuation 25
 
0.6%
Open Punctuation 25
 
0.6%
Other Punctuation 19
 
0.5%
Dash Punctuation 6
 
0.2%
Connector Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 146
 
7.4%
방 110
 
5.6%
사 94
 
4.8%
기 86
 
4.4%
시 80
 
4.1%
주 50
 
2.5%
보 48
 
2.4%
서 47
 
2.4%
전 36
 
1.8%
이 35
 
1.8%
Other values (221) 1237
62.8%
Uppercase Letter
ValueCountFrequency (%)
E 55
16.1%
T 37
10.9%
R 35
10.3%
S 35
10.3%
U 31
9.1%
A 22
 
6.5%
M 20
 
5.9%
L 16
 
4.7%
F 16
 
4.7%
C 13
 
3.8%
Other values (13) 61
17.9%
Lowercase Letter
ValueCountFrequency (%)
e 28
13.3%
o 25
11.8%
c 17
 
8.1%
a 17
 
8.1%
r 16
 
7.6%
l 16
 
7.6%
m 15
 
7.1%
n 13
 
6.2%
u 11
 
5.2%
s 9
 
4.3%
Other values (13) 44
20.9%
Decimal Number
ValueCountFrequency (%)
6 223
19.0%
3 210
17.9%
5 163
13.9%
0 150
12.8%
4 149
12.7%
1 111
9.4%
2 107
9.1%
7 24
 
2.0%
8 22
 
1.9%
9 16
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 15
78.9%
/ 2
 
10.5%
, 1
 
5.3%
; 1
 
5.3%
Math Symbol
ValueCountFrequency (%)
+ 1
50.0%
> 1
50.0%
Space Separator
ValueCountFrequency (%)
210
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1969
49.4%
Common 1464
36.7%
Latin 552
 
13.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 146
 
7.4%
방 110
 
5.6%
사 94
 
4.8%
기 86
 
4.4%
시 80
 
4.1%
주 50
 
2.5%
보 48
 
2.4%
서 47
 
2.4%
전 36
 
1.8%
이 35
 
1.8%
Other values (221) 1237
62.8%
Latin
ValueCountFrequency (%)
E 55
 
10.0%
T 37
 
6.7%
R 35
 
6.3%
S 35
 
6.3%
U 31
 
5.6%
e 28
 
5.1%
o 25
 
4.5%
A 22
 
4.0%
M 20
 
3.6%
c 17
 
3.1%
Other values (36) 247
44.7%
Common
ValueCountFrequency (%)
6 223
15.2%
210
14.3%
3 210
14.3%
5 163
11.1%
0 150
10.2%
4 149
10.2%
1 111
7.6%
2 107
7.3%
) 25
 
1.7%
( 25
 
1.7%
Other values (11) 91
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2016
50.6%
Hangul 1969
49.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 223
 
11.1%
210
 
10.4%
3 210
 
10.4%
5 163
 
8.1%
0 150
 
7.4%
4 149
 
7.4%
1 111
 
5.5%
2 107
 
5.3%
E 55
 
2.7%
T 37
 
1.8%
Other values (57) 601
29.8%
Hangul
ValueCountFrequency (%)
지 146
 
7.4%
방 110
 
5.6%
사 94
 
4.8%
기 86
 
4.4%
시 80
 
4.1%
주 50
 
2.5%
보 48
 
2.4%
서 47
 
2.4%
전 36
 
1.8%
이 35
 
1.8%
Other values (221) 1237
62.8%

코드설명
Text

MISSING 

Distinct108
Distinct (%)61.0%
Missing350
Missing (%)66.4%
Memory size4.2 KiB
2023-12-12T19:26:51.519095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length7.3446328
Min length2

Characters and Unicode

Total characters1300
Distinct characters193
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)35.6%

Sample

1st row관리부서
2nd row민원행정
3rd row새올-인터넷민원용
4th row암호 초기값
5th row관리자가 부서업무 문안 보임
ValueCountFrequency (%)
표시여부 7
 
2.6%
민원행정 7
 
2.6%
사용여부 4
 
1.5%
대명3동 3
 
1.1%
대명9동 3
 
1.1%
대명6동 3
 
1.1%
대명5동 3
 
1.1%
statistics_visible 3
 
1.1%
민원행정과 3
 
1.1%
조직도 3
 
1.1%
Other values (144) 228
85.4%
2023-12-12T19:26:52.022318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
90
 
6.9%
과 48
 
3.7%
동 39
 
3.0%
대 38
 
2.9%
원 34
 
2.6%
시 33
 
2.5%
정 32
 
2.5%
민 30
 
2.3%
부 29
 
2.2%
지 28
 
2.2%
Other values (183) 899
69.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1064
81.8%
Space Separator 90
 
6.9%
Uppercase Letter 73
 
5.6%
Decimal Number 42
 
3.2%
Lowercase Letter 19
 
1.5%
Dash Punctuation 3
 
0.2%
Connector Punctuation 3
 
0.2%
Close Punctuation 2
 
0.2%
Open Punctuation 2
 
0.2%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 48
 
4.5%
동 39
 
3.7%
대 38
 
3.6%
원 34
 
3.2%
시 33
 
3.1%
정 32
 
3.0%
민 30
 
2.8%
부 29
 
2.7%
지 28
 
2.6%
명 27
 
2.5%
Other values (147) 726
68.2%
Uppercase Letter
ValueCountFrequency (%)
S 20
27.4%
I 12
16.4%
T 9
12.3%
M 8
 
11.0%
A 5
 
6.8%
B 4
 
5.5%
E 3
 
4.1%
L 3
 
4.1%
V 3
 
4.1%
C 3
 
4.1%
Other values (2) 3
 
4.1%
Lowercase Letter
ValueCountFrequency (%)
e 5
26.3%
t 4
21.1%
y 3
15.8%
b 2
 
10.5%
u 1
 
5.3%
r 1
 
5.3%
g 1
 
5.3%
a 1
 
5.3%
p 1
 
5.3%
Decimal Number
ValueCountFrequency (%)
1 15
35.7%
2 6
 
14.3%
3 6
 
14.3%
0 3
 
7.1%
5 3
 
7.1%
6 3
 
7.1%
9 3
 
7.1%
4 3
 
7.1%
Other Punctuation
ValueCountFrequency (%)
/ 1
50.0%
: 1
50.0%
Space Separator
ValueCountFrequency (%)
90
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1064
81.8%
Common 144
 
11.1%
Latin 92
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 48
 
4.5%
동 39
 
3.7%
대 38
 
3.6%
원 34
 
3.2%
시 33
 
3.1%
정 32
 
3.0%
민 30
 
2.8%
부 29
 
2.7%
지 28
 
2.6%
명 27
 
2.5%
Other values (147) 726
68.2%
Latin
ValueCountFrequency (%)
S 20
21.7%
I 12
13.0%
T 9
9.8%
M 8
 
8.7%
e 5
 
5.4%
A 5
 
5.4%
B 4
 
4.3%
t 4
 
4.3%
y 3
 
3.3%
E 3
 
3.3%
Other values (11) 19
20.7%
Common
ValueCountFrequency (%)
90
62.5%
1 15
 
10.4%
2 6
 
4.2%
3 6
 
4.2%
- 3
 
2.1%
0 3
 
2.1%
_ 3
 
2.1%
5 3
 
2.1%
6 3
 
2.1%
9 3
 
2.1%
Other values (5) 9
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1064
81.8%
ASCII 236
 
18.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
90
38.1%
S 20
 
8.5%
1 15
 
6.4%
I 12
 
5.1%
T 9
 
3.8%
M 8
 
3.4%
2 6
 
2.5%
3 6
 
2.5%
e 5
 
2.1%
A 5
 
2.1%
Other values (26) 60
25.4%
Hangul
ValueCountFrequency (%)
과 48
 
4.5%
동 39
 
3.7%
대 38
 
3.6%
원 34
 
3.2%
시 33
 
3.1%
정 32
 
3.0%
민 30
 
2.8%
부 29
 
2.7%
지 28
 
2.6%
명 27
 
2.5%
Other values (147) 726
68.2%

Interactions

2023-12-12T19:26:48.465899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-12T19:26:48.559541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:26:48.650280image/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.
2023-12-12T19:26:48.730629image/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

주코드부코드코드명코드설명
00<NA>시스템<NA>
101민원정보과관리부서
20320111100000000민원행정
30420080100000000새올-인터넷민원용
4051암호 초기값
507Y관리자가 부서업무 문안 보임
60853지역번호
709664국번
8010TRUE개별업무 회신번호 수정가능여부
9011TRUE부서업무 회신번호 수정가능여부
주코드부코드코드명코드설명
5171073440069536642314민원행정과
5181073440070536642514주민생활지원국
5191073440071536642514주민생활지원과
5201073440072536642501복지지원과
5211073440073536642641지역경제과
5221073440074536642714환경관리과
5231073440075536642753위생과
524108<NA>발송현황-자동문안삭제<NA>
525109<NA>세정관련 교체코드<NA>
526210<NA>민원행정 발송금지 부서<NA>