Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells715
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory800.8 KiB
Average record size in memory82.0 B

Variable types

Categorical2
Text5
Numeric2

Dataset

Description대분류코드,소분류코드,적용시작일,코드명,사용여부,코드설명,적용만료일,상위코드,정렬순서
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15409/S/1/datasetView.do

Alerts

적용만료일 is highly overall correlated with 사용여부High correlation
사용여부 is highly overall correlated with 적용만료일High correlation
대분류코드 is highly imbalanced (95.0%)Imbalance
상위코드 has 127 (1.3%) missing valuesMissing
정렬순서 has 498 (5.0%) missing valuesMissing

Reproduction

Analysis started2024-05-11 05:50:29.643521
Analysis finished2024-05-11 05:50:56.263846
Duration26.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류코드
Categorical

IMBALANCE 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
CM040
9781 
CM024
 
134
CM039
 
27
DJ020
 
16
CM004
 
9
Other values (11)
 
33

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowCM040
2nd rowCM040
3rd rowCM040
4th rowCM040
5th rowCM040

Common Values

ValueCountFrequency (%)
CM040 9781
97.8%
CM024 134
 
1.3%
CM039 27
 
0.3%
DJ020 16
 
0.2%
CM004 9
 
0.1%
CDLST 8
 
0.1%
CM035 6
 
0.1%
DJ008 4
 
< 0.1%
CM003 4
 
< 0.1%
NA001 2
 
< 0.1%
Other values (6) 9
 
0.1%

Length

2024-05-11T05:50:56.499615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cm040 9781
97.8%
cm024 134
 
1.3%
cm039 27
 
0.3%
dj020 16
 
0.2%
cm004 9
 
0.1%
cdlst 8
 
0.1%
cm035 6
 
0.1%
dj008 4
 
< 0.1%
cm003 4
 
< 0.1%
na001 2
 
< 0.1%
Other values (6) 9
 
0.1%

소분류코드
Text

Distinct9989
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T05:50:57.257415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9406
Min length1

Characters and Unicode

Total characters69406
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

Unique9982 ?
Unique (%)99.8%

Sample

1st row6111493
2nd row3510032
3rd row5310041
4th row6271285
5th row6280717
ValueCountFrequency (%)
1 5
 
< 0.1%
20 3
 
< 0.1%
2 2
 
< 0.1%
08 2
 
< 0.1%
6440703 2
 
< 0.1%
4 2
 
< 0.1%
6440539 2
 
< 0.1%
5060198 1
 
< 0.1%
4820071 1
 
< 0.1%
5100244 1
 
< 0.1%
Other values (9979) 9979
99.8%
2024-05-11T05:50:58.594498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16938
24.4%
1 8249
11.9%
3 7528
10.8%
4 7501
10.8%
6 6523
 
9.4%
5 5992
 
8.6%
2 5950
 
8.6%
8 3672
 
5.3%
9 3558
 
5.1%
7 3459
 
5.0%
Other values (5) 36
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69370
99.9%
Uppercase Letter 36
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16938
24.4%
1 8249
11.9%
3 7528
10.9%
4 7501
10.8%
6 6523
 
9.4%
5 5992
 
8.6%
2 5950
 
8.6%
8 3672
 
5.3%
9 3558
 
5.1%
7 3459
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
Z 20
55.6%
C 5
 
13.9%
M 5
 
13.9%
D 3
 
8.3%
J 3
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 69370
99.9%
Latin 36
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16938
24.4%
1 8249
11.9%
3 7528
10.9%
4 7501
10.8%
6 6523
 
9.4%
5 5992
 
8.6%
2 5950
 
8.6%
8 3672
 
5.3%
9 3558
 
5.1%
7 3459
 
5.0%
Latin
ValueCountFrequency (%)
Z 20
55.6%
C 5
 
13.9%
M 5
 
13.9%
D 3
 
8.3%
J 3
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69406
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16938
24.4%
1 8249
11.9%
3 7528
10.8%
4 7501
10.8%
6 6523
 
9.4%
5 5992
 
8.6%
2 5950
 
8.6%
8 3672
 
5.3%
9 3558
 
5.1%
7 3459
 
5.0%
Other values (5) 36
 
0.1%

적용시작일
Text

Distinct2534
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T05:50:59.372143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9997
Min length7

Characters and Unicode

Total characters79997
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1110 ?
Unique (%)11.1%

Sample

1st row20050105
2nd row19900101
3rd row19950101
4th row20220719
5th row20050201
ValueCountFrequency (%)
19900101 592
 
5.9%
19950101 164
 
1.6%
19880423 139
 
1.4%
20060701 88
 
0.9%
20080101 80
 
0.8%
20070101 79
 
0.8%
20240103 61
 
0.6%
20141229 60
 
0.6%
20240101 59
 
0.6%
20120101 53
 
0.5%
Other values (2526) 8627
86.3%
2024-05-11T05:51:00.588616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 25712
32.1%
1 17365
21.7%
2 14946
18.7%
9 6222
 
7.8%
8 3479
 
4.3%
7 2878
 
3.6%
3 2801
 
3.5%
6 2268
 
2.8%
5 2254
 
2.8%
4 2055
 
2.6%
Other values (2) 17
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79980
> 99.9%
Space Separator 14
 
< 0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25712
32.1%
1 17365
21.7%
2 14946
18.7%
9 6222
 
7.8%
8 3479
 
4.3%
7 2878
 
3.6%
3 2801
 
3.5%
6 2268
 
2.8%
5 2254
 
2.8%
4 2055
 
2.6%
Space Separator
ValueCountFrequency (%)
14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 79997
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25712
32.1%
1 17365
21.7%
2 14946
18.7%
9 6222
 
7.8%
8 3479
 
4.3%
7 2878
 
3.6%
3 2801
 
3.5%
6 2268
 
2.8%
5 2254
 
2.8%
4 2055
 
2.6%
Other values (2) 17
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25712
32.1%
1 17365
21.7%
2 14946
18.7%
9 6222
 
7.8%
8 3479
 
4.3%
7 2878
 
3.6%
3 2801
 
3.5%
6 2268
 
2.8%
5 2254
 
2.8%
4 2055
 
2.6%
Other values (2) 17
 
< 0.1%

코드명
Text

Distinct4443
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T05:51:01.331683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length5.0789
Min length1

Characters and Unicode

Total characters50789
Distinct characters506
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

Unique3541 ?
Unique (%)35.4%

Sample

1st row진상규명추진반
2nd row학익2동
3rd row진주성관리사무소
4th row환경정책과
5th row대중교통과
ValueCountFrequency (%)
사회복지과 116
 
1.2%
총무과 104
 
1.0%
건축과 94
 
0.9%
건설과 92
 
0.9%
자치행정과 86
 
0.9%
지역경제과 74
 
0.7%
회계과 68
 
0.7%
주민생활지원과 64
 
0.6%
환경위생과 60
 
0.6%
재무과 57
 
0.6%
Other values (4440) 9254
91.9%
2024-05-11T05:51:02.511405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 5997
 
11.8%
지 1548
 
3.0%
정 1341
 
2.6%
관 1294
 
2.5%
원 1091
 
2.1%
도 1052
 
2.1%
소 1026
 
2.0%
사 1004
 
2.0%
시 963
 
1.9%
경 899
 
1.8%
Other values (496) 34574
68.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 50085
98.6%
Decimal Number 498
 
1.0%
Space Separator 74
 
0.1%
Uppercase Letter 33
 
0.1%
Close Punctuation 22
 
< 0.1%
Open Punctuation 22
 
< 0.1%
Connector Punctuation 19
 
< 0.1%
Other Punctuation 17
 
< 0.1%
Lowercase Letter 12
 
< 0.1%
Dash Punctuation 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 5997
 
12.0%
지 1548
 
3.1%
정 1341
 
2.7%
관 1294
 
2.6%
원 1091
 
2.2%
도 1052
 
2.1%
소 1026
 
2.0%
사 1004
 
2.0%
시 963
 
1.9%
경 899
 
1.8%
Other values (459) 33870
67.6%
Uppercase Letter
ValueCountFrequency (%)
U 5
15.2%
I 5
15.2%
T 4
12.1%
C 4
12.1%
E 3
9.1%
A 2
 
6.1%
F 2
 
6.1%
O 2
 
6.1%
S 2
 
6.1%
P 1
 
3.0%
Other values (3) 3
9.1%
Decimal Number
ValueCountFrequency (%)
1 206
41.4%
2 147
29.5%
3 58
 
11.6%
9 37
 
7.4%
4 17
 
3.4%
5 11
 
2.2%
6 8
 
1.6%
7 5
 
1.0%
8 5
 
1.0%
0 4
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
t 3
25.0%
y 3
25.0%
i 3
25.0%
c 2
16.7%
o 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
. 9
52.9%
, 4
23.5%
? 3
 
17.6%
/ 1
 
5.9%
Space Separator
ValueCountFrequency (%)
74
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 19
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 50085
98.6%
Common 659
 
1.3%
Latin 45
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 5997
 
12.0%
지 1548
 
3.1%
정 1341
 
2.7%
관 1294
 
2.6%
원 1091
 
2.2%
도 1052
 
2.1%
소 1026
 
2.0%
사 1004
 
2.0%
시 963
 
1.9%
경 899
 
1.8%
Other values (459) 33870
67.6%
Common
ValueCountFrequency (%)
1 206
31.3%
2 147
22.3%
74
 
11.2%
3 58
 
8.8%
9 37
 
5.6%
) 22
 
3.3%
( 22
 
3.3%
_ 19
 
2.9%
4 17
 
2.6%
5 11
 
1.7%
Other values (9) 46
 
7.0%
Latin
ValueCountFrequency (%)
U 5
11.1%
I 5
11.1%
T 4
 
8.9%
C 4
 
8.9%
E 3
 
6.7%
t 3
 
6.7%
y 3
 
6.7%
i 3
 
6.7%
A 2
 
4.4%
F 2
 
4.4%
Other values (8) 11
24.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 50085
98.6%
ASCII 704
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 5997
 
12.0%
지 1548
 
3.1%
정 1341
 
2.7%
관 1294
 
2.6%
원 1091
 
2.2%
도 1052
 
2.1%
소 1026
 
2.0%
사 1004
 
2.0%
시 963
 
1.9%
경 899
 
1.8%
Other values (459) 33870
67.6%
ASCII
ValueCountFrequency (%)
1 206
29.3%
2 147
20.9%
74
 
10.5%
3 58
 
8.2%
9 37
 
5.3%
) 22
 
3.1%
( 22
 
3.1%
_ 19
 
2.7%
4 17
 
2.4%
5 11
 
1.6%
Other values (27) 91
12.9%

사용여부
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
5688 
1
4312 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 5688
56.9%
1 4312
43.1%

Length

2024-05-11T05:51:02.955744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:51:03.257221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5688
56.9%
1 4312
43.1%

코드설명
Text

Distinct9672
Distinct (%)97.4%
Missing72
Missing (%)0.7%
Memory size156.2 KiB
2024-05-11T05:51:04.034308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length33
Mean length17.937349
Min length4

Characters and Unicode

Total characters178082
Distinct characters511
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

Unique9576 ?
Unique (%)96.5%

Sample

1st row서울특별시 행정국 진상규명추진반
2nd row인천광역시 남구 학익2동
3rd row경상남도 진주시 진주성관리사무소
4th row대구광역시 환경수자원국 환경정책과
5th row인천광역시 교통국 대중교통과
ValueCountFrequency (%)
경기도 1899
 
5.4%
서울특별시 1254
 
3.6%
경상남도 767
 
2.2%
경상북도 741
 
2.1%
전라남도 618
 
1.8%
충청남도 582
 
1.7%
부산광역시 553
 
1.6%
강원도 537
 
1.5%
전라북도 536
 
1.5%
인천광역시 495
 
1.4%
Other values (5383) 26895
77.1%
2024-05-11T05:51:05.782708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24957
 
14.0%
시 9521
 
5.3%
도 8703
 
4.9%
과 6117
 
3.4%
경 5710
 
3.2%
국 4654
 
2.6%
기 3248
 
1.8%
구 3149
 
1.8%
광 3019
 
1.7%
원 2781
 
1.6%
Other values (501) 106223
59.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 150635
84.6%
Space Separator 24957
 
14.0%
Decimal Number 1635
 
0.9%
Other Punctuation 390
 
0.2%
Lowercase Letter 384
 
0.2%
Uppercase Letter 46
 
< 0.1%
Open Punctuation 12
 
< 0.1%
Close Punctuation 12
 
< 0.1%
Dash Punctuation 8
 
< 0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 9521
 
6.3%
도 8703
 
5.8%
과 6117
 
4.1%
경 5710
 
3.8%
국 4654
 
3.1%
기 3248
 
2.2%
구 3149
 
2.1%
광 3019
 
2.0%
원 2781
 
1.8%
산 2762
 
1.8%
Other values (454) 100971
67.0%
Uppercase Letter
ValueCountFrequency (%)
C 6
13.0%
I 6
13.0%
T 6
13.0%
U 5
10.9%
E 3
 
6.5%
F 2
 
4.3%
O 2
 
4.3%
D 2
 
4.3%
G 2
 
4.3%
S 2
 
4.3%
Other values (9) 10
21.7%
Decimal Number
ValueCountFrequency (%)
0 462
28.3%
2 378
23.1%
1 349
21.3%
3 82
 
5.0%
5 80
 
4.9%
9 72
 
4.4%
6 67
 
4.1%
8 64
 
3.9%
4 59
 
3.6%
7 22
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
c 95
24.7%
o 94
24.5%
v 93
24.2%
n 93
24.2%
i 3
 
0.8%
t 3
 
0.8%
y 3
 
0.8%
Other Punctuation
ValueCountFrequency (%)
/ 185
47.4%
: 120
30.8%
. 80
20.5%
? 3
 
0.8%
, 2
 
0.5%
Space Separator
ValueCountFrequency (%)
24957
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Math Symbol
ValueCountFrequency (%)
> 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 150635
84.6%
Common 27017
 
15.2%
Latin 430
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 9521
 
6.3%
도 8703
 
5.8%
과 6117
 
4.1%
경 5710
 
3.8%
국 4654
 
3.1%
기 3248
 
2.2%
구 3149
 
2.1%
광 3019
 
2.0%
원 2781
 
1.8%
산 2762
 
1.8%
Other values (454) 100971
67.0%
Latin
ValueCountFrequency (%)
c 95
22.1%
o 94
21.9%
v 93
21.6%
n 93
21.6%
C 6
 
1.4%
I 6
 
1.4%
T 6
 
1.4%
U 5
 
1.2%
i 3
 
0.7%
t 3
 
0.7%
Other values (16) 26
 
6.0%
Common
ValueCountFrequency (%)
24957
92.4%
0 462
 
1.7%
2 378
 
1.4%
1 349
 
1.3%
/ 185
 
0.7%
: 120
 
0.4%
3 82
 
0.3%
. 80
 
0.3%
5 80
 
0.3%
9 72
 
0.3%
Other values (11) 252
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 150635
84.6%
ASCII 27447
 
15.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24957
90.9%
0 462
 
1.7%
2 378
 
1.4%
1 349
 
1.3%
/ 185
 
0.7%
: 120
 
0.4%
c 95
 
0.3%
o 94
 
0.3%
v 93
 
0.3%
n 93
 
0.3%
Other values (37) 621
 
2.3%
Hangul
ValueCountFrequency (%)
시 9521
 
6.3%
도 8703
 
5.8%
과 6117
 
4.1%
경 5710
 
3.8%
국 4654
 
3.1%
기 3248
 
2.2%
구 3149
 
2.1%
광 3019
 
2.0%
원 2781
 
1.8%
산 2762
 
1.8%
Other values (454) 100971
67.0%

적용만료일
Real number (ℝ)

HIGH CORRELATION 

Distinct1625
Distinct (%)16.3%
Missing18
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean55024699
Minimum19681008
Maximum99991231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:51:06.347114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19681008
5-th percentile20030120
Q120091102
median20190306
Q399991231
95-th percentile99991231
Maximum99991231
Range80310223
Interquartile range (IQR)79900129

Descriptive statistics

Standard deviation39625479
Coefficient of variation (CV)0.72013986
Kurtosis-1.9360172
Mean55024699
Median Absolute Deviation (MAD)159380
Skewness0.25370315
Sum5.4925655 × 1011
Variance1.5701786 × 1015
MonotonicityNot monotonic
2024-05-11T05:51:07.304558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99991231 4363
43.6%
20060701 97
 
1.0%
20070101 71
 
0.7%
19991231 68
 
0.7%
20110412 63
 
0.6%
20080101 59
 
0.6%
20190101 56
 
0.6%
20150101 48
 
0.5%
20100630 45
 
0.4%
20181231 39
 
0.4%
Other values (1615) 5073
50.7%
ValueCountFrequency (%)
19681008 1
 
< 0.1%
19770223 1
 
< 0.1%
19811109 1
 
< 0.1%
19831231 1
 
< 0.1%
19860401 2
< 0.1%
19920115 1
 
< 0.1%
19931231 3
< 0.1%
19950905 1
 
< 0.1%
19961011 2
< 0.1%
19980825 1
 
< 0.1%
ValueCountFrequency (%)
99991231 4363
43.6%
20240430 1
 
< 0.1%
20240408 1
 
< 0.1%
20240403 4
 
< 0.1%
20240401 1
 
< 0.1%
20240328 2
 
< 0.1%
20240307 9
 
0.1%
20240305 1
 
< 0.1%
20240302 2
 
< 0.1%
20240201 2
 
< 0.1%

상위코드
Text

MISSING 

Distinct4152
Distinct (%)42.1%
Missing127
Missing (%)1.3%
Memory size156.2 KiB
2024-05-11T05:51:08.346087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9637395
Min length2

Characters and Unicode

Total characters68753
Distinct characters11
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

Unique2140 ?
Unique (%)21.7%

Sample

1st row6110911
2nd row3510000
3rd row5310000
4th row6271253
5th row6280715
ValueCountFrequency (%)
6110000 40
 
0.4%
6260000 35
 
0.4%
6410000 32
 
0.3%
6480000 27
 
0.3%
6450000 27
 
0.3%
6290000 26
 
0.3%
3860000 24
 
0.2%
3930000 24
 
0.2%
4060000 23
 
0.2%
6280000 23
 
0.2%
Other values (4142) 9592
97.2%
2024-05-11T05:51:10.544992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 25159
36.6%
1 6631
 
9.6%
4 6463
 
9.4%
3 6417
 
9.3%
6 5685
 
8.3%
5 5163
 
7.5%
2 4698
 
6.8%
8 2967
 
4.3%
9 2837
 
4.1%
7 2714
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68734
> 99.9%
Uppercase Letter 19
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25159
36.6%
1 6631
 
9.6%
4 6463
 
9.4%
3 6417
 
9.3%
6 5685
 
8.3%
5 5163
 
7.5%
2 4698
 
6.8%
8 2967
 
4.3%
9 2837
 
4.1%
7 2714
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
Z 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68734
> 99.9%
Latin 19
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25159
36.6%
1 6631
 
9.6%
4 6463
 
9.4%
3 6417
 
9.3%
6 5685
 
8.3%
5 5163
 
7.5%
2 4698
 
6.8%
8 2967
 
4.3%
9 2837
 
4.1%
7 2714
 
3.9%
Latin
ValueCountFrequency (%)
Z 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68753
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25159
36.6%
1 6631
 
9.6%
4 6463
 
9.4%
3 6417
 
9.3%
6 5685
 
8.3%
5 5163
 
7.5%
2 4698
 
6.8%
8 2967
 
4.3%
9 2837
 
4.1%
7 2714
 
3.9%

정렬순서
Real number (ℝ)

MISSING 

Distinct9428
Distinct (%)99.2%
Missing498
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean28104.044
Minimum0
Maximum61409
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T05:51:11.207261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1712.15
Q113289.5
median27857.5
Q342471.5
95-th percentile54675.95
Maximum61409
Range61409
Interquartile range (IQR)29182

Descriptive statistics

Standard deviation17049.8
Coefficient of variation (CV)0.60666713
Kurtosis-1.1377445
Mean28104.044
Median Absolute Deviation (MAD)14585
Skewness0.058836491
Sum2.6704463 × 108
Variance2.9069568 × 108
MonotonicityNot monotonic
2024-05-11T05:51:11.772529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7
 
0.1%
36432 7
 
0.1%
2 5
 
0.1%
3 4
 
< 0.1%
4 3
 
< 0.1%
528 3
 
< 0.1%
0 3
 
< 0.1%
8 3
 
< 0.1%
41 3
 
< 0.1%
63 2
 
< 0.1%
Other values (9418) 9462
94.6%
(Missing) 498
 
5.0%
ValueCountFrequency (%)
0 3
< 0.1%
1 7
0.1%
2 5
0.1%
3 4
< 0.1%
4 3
< 0.1%
7 2
 
< 0.1%
8 3
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
61409 1
< 0.1%
61407 1
< 0.1%
61401 1
< 0.1%
61393 1
< 0.1%
61391 1
< 0.1%
61388 1
< 0.1%
61378 1
< 0.1%
61377 1
< 0.1%
61375 1
< 0.1%
61365 1
< 0.1%

Interactions

2024-05-11T05:50:47.395731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:50:33.241448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:50:54.428669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:50:42.805689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T05:51:12.137277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류코드사용여부적용만료일정렬순서
대분류코드1.0000.1510.1480.306
사용여부0.1511.0001.0000.636
적용만료일0.1481.0001.0000.638
정렬순서0.3060.6360.6381.000
2024-05-11T05:51:12.400181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류코드사용여부
대분류코드1.0000.118
사용여부0.1181.000
2024-05-11T05:51:12.666883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
적용만료일정렬순서대분류코드사용여부
적용만료일1.0000.4350.0000.900
정렬순서0.4351.0000.1240.493
대분류코드0.0000.1241.0000.118
사용여부0.9000.4930.1181.000

Missing values

2024-05-11T05:50:54.955875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T05:50:55.693635image/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.
2024-05-11T05:50:56.074028image/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

대분류코드소분류코드적용시작일코드명사용여부코드설명적용만료일상위코드정렬순서
47383CM040611149320050105진상규명추진반0서울특별시 행정국 진상규명추진반20050105611091128771
9151CM040351003219900101학익2동0인천광역시 남구 학익2동2018063035100005479
39572CM040531004119950101진주성관리사무소0경상남도 진주시 진주성관리사무소20110102531000024234
51616CM040627128520220719환경정책과1대구광역시 환경수자원국 환경정책과999912316271253<NA>
52092CM040628071720050201대중교통과0인천광역시 교통국 대중교통과20071022628071531387
34177CM040494005119980915총무과0전라남도 영암군 총무과20040325494000020832
43828CM040556002420080222도시미관과0경기도 안산시 단원구 도시미관과20090209556000026636
27374CM040451010320050225조사특별위원회1충청남도 보령시 의회 조사특별위원회99991231451009716398
38301CM040519004119750115부군수1경상북도 청도군 부군수99991231519000023305
19027CM040401038220201223정책기획과1경기도 시흥시 기획조정실 정책기획과99991231401038053569
대분류코드소분류코드적용시작일코드명사용여부코드설명적용만료일상위코드정렬순서
812CM030120050101전유1<NA>99991231<NA>1
22349CM040418012220030101체육진흥과0강원도 춘천시 관광문화국 체육진흥과20060701418011913266
48680CM040611320220120928공원기획과0서울특별시 한강사업본부 공원부 공원기획과20190101611315449164
59158CM040649020720050706프로젝트담당관0제주도 제주특별자치도추진기획단 프로젝트담당관20060701649020535009
50175CM040626115320141224의료산업과0부산광역시 건강체육국 의료산업과20180914626110637632
20835CM040407016220180406정보통신과1경기도 이천시 자치행정국 정보통신과99991231407015343235
5577CM040324006620010101기획예산과0서울특별시 강동구 행정관리국 기획예산과2003090132400643144
36207CM040507002019981012연구개발과0경상북도 안동시 농업기술센터 연구개발과20080929507001722022
1344CM040301013920130226건강관리과1서울특별시 중구 보건소 건강관리과999912313010033243
22063CM040416017120220715치매안심센터1경기도 가평군 보건소 치매안심센터999912314160015<NA>