Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells10000
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory128.0 B

Variable types

Categorical3
Text4
Numeric6
Unsupported1

Dataset

Description회계년도,세부사업코드,세부사업명,부서코드,부서명,목세목코드,목세목명,적요,지급처리일,지급금액,분야코드,분야명,부문코드,부문명
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-12076/S/1/datasetView.do

Alerts

회계년도 has constant value ""Constant
부문명 is highly overall correlated with 분야코드 and 2 other fieldsHigh correlation
분야명 is highly overall correlated with 분야코드 and 2 other fieldsHigh correlation
분야코드 is highly overall correlated with 부문코드 and 2 other fieldsHigh correlation
부문코드 is highly overall correlated with 분야코드 and 2 other fieldsHigh correlation
적요 has 10000 (100.0%) missing valuesMissing
지급금액 is highly skewed (γ1 = 46.15368126)Skewed
적요 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-11 09:46:37.468536
Analysis finished2024-05-11 09:46:56.378073
Duration18.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 10000
100.0%

Length

2024-05-11T09:46:56.574886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:46:56.871362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024 10000
100.0%

세부사업코드
Text

Distinct1561
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T09:46:57.537719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters160000
Distinct characters16
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

Unique534 ?
Unique (%)5.3%

Sample

1st row6110000200831489
2nd row611000020213057A
3rd row61100002016304C8
4th row61100002016304C8
5th row61100002015305EE
ValueCountFrequency (%)
61100002016304c8 1593
 
15.9%
6110000200830666 378
 
3.8%
6110000201430319 230
 
2.3%
61100002016304c2 178
 
1.8%
6110000200830379 125
 
1.2%
6110000200832072 123
 
1.2%
61100002016304bd 98
 
1.0%
6110000200831299 69
 
0.7%
61100002016304de 55
 
0.5%
6110000200831580 55
 
0.5%
Other values (1551) 7096
71.0%
2024-05-11T09:46:58.800015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 64277
40.2%
1 29458
18.4%
6 15348
 
9.6%
2 14407
 
9.0%
3 12709
 
7.9%
8 7604
 
4.8%
4 4576
 
2.9%
9 2356
 
1.5%
5 2304
 
1.4%
C 2258
 
1.4%
Other values (6) 4703
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 155186
97.0%
Uppercase Letter 4814
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64277
41.4%
1 29458
19.0%
6 15348
 
9.9%
2 14407
 
9.3%
3 12709
 
8.2%
8 7604
 
4.9%
4 4576
 
2.9%
9 2356
 
1.5%
5 2304
 
1.5%
7 2147
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
C 2258
46.9%
D 730
 
15.2%
E 507
 
10.5%
F 484
 
10.1%
B 453
 
9.4%
A 382
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common 155186
97.0%
Latin 4814
 
3.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64277
41.4%
1 29458
19.0%
6 15348
 
9.9%
2 14407
 
9.3%
3 12709
 
8.2%
8 7604
 
4.9%
4 4576
 
2.9%
9 2356
 
1.5%
5 2304
 
1.5%
7 2147
 
1.4%
Latin
ValueCountFrequency (%)
C 2258
46.9%
D 730
 
15.2%
E 507
 
10.5%
F 484
 
10.1%
B 453
 
9.4%
A 382
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64277
40.2%
1 29458
18.4%
6 15348
 
9.6%
2 14407
 
9.0%
3 12709
 
7.9%
8 7604
 
4.8%
4 4576
 
2.9%
9 2356
 
1.5%
5 2304
 
1.4%
C 2258
 
1.4%
Other values (6) 4703
 
2.9%

세부사업명
Text

Distinct1378
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T09:46:59.454644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length31
Mean length10.1954
Min length4

Characters and Unicode

Total characters101954
Distinct characters540
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique515 ?
Unique (%)5.1%

Sample

1st row기본경비
2nd row기본경비
3rd row기본경비
4th row기본경비
5th row기본경비
ValueCountFrequency (%)
기본경비 3169
 
14.1%
운영 1380
 
6.1%
및 1102
 
4.9%
유지관리 703
 
3.1%
지원 533
 
2.4%
인력운영비(통합편성 378
 
1.7%
서울형 286
 
1.3%
뉴딜일자리 230
 
1.0%
관리 224
 
1.0%
등 207
 
0.9%
Other values (2277) 14232
63.4%
2024-05-11T09:47:00.728268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12468
 
12.2%
비 4588
 
4.5%
기 4002
 
3.9%
경 3634
 
3.6%
본 3362
 
3.3%
지 2720
 
2.7%
원 2336
 
2.3%
운 2300
 
2.3%
영 2287
 
2.2%
관 1991
 
2.0%
Other values (530) 62266
61.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 86593
84.9%
Space Separator 12468
 
12.2%
Close Punctuation 1030
 
1.0%
Open Punctuation 1030
 
1.0%
Decimal Number 303
 
0.3%
Other Punctuation 217
 
0.2%
Uppercase Letter 135
 
0.1%
Dash Punctuation 132
 
0.1%
Lowercase Letter 38
 
< 0.1%
Math Symbol 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 4588
 
5.3%
기 4002
 
4.6%
경 3634
 
4.2%
본 3362
 
3.9%
지 2720
 
3.1%
원 2336
 
2.7%
운 2300
 
2.7%
영 2287
 
2.6%
관 1991
 
2.3%
리 1903
 
2.2%
Other values (480) 57470
66.4%
Uppercase Letter
ValueCountFrequency (%)
I 25
18.5%
T 19
14.1%
C 16
11.9%
G 12
8.9%
S 10
 
7.4%
A 9
 
6.7%
M 8
 
5.9%
D 8
 
5.9%
P 6
 
4.4%
E 6
 
4.4%
Other values (7) 16
11.9%
Lowercase Letter
ValueCountFrequency (%)
a 8
21.1%
k 4
10.5%
c 4
10.5%
r 4
10.5%
t 4
10.5%
s 4
10.5%
e 4
10.5%
o 2
 
5.3%
n 2
 
5.3%
u 1
 
2.6%
Decimal Number
ValueCountFrequency (%)
6 134
44.2%
1 52
 
17.2%
0 25
 
8.3%
7 24
 
7.9%
4 18
 
5.9%
9 18
 
5.9%
2 14
 
4.6%
5 6
 
2.0%
8 6
 
2.0%
3 6
 
2.0%
Other Punctuation
ValueCountFrequency (%)
? 128
59.0%
, 85
39.2%
. 2
 
0.9%
& 2
 
0.9%
Close Punctuation
ValueCountFrequency (%)
) 1026
99.6%
」 4
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 1026
99.6%
「 4
 
0.4%
Math Symbol
ValueCountFrequency (%)
+ 7
87.5%
~ 1
 
12.5%
Space Separator
ValueCountFrequency (%)
12468
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 86593
84.9%
Common 15188
 
14.9%
Latin 173
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 4588
 
5.3%
기 4002
 
4.6%
경 3634
 
4.2%
본 3362
 
3.9%
지 2720
 
3.1%
원 2336
 
2.7%
운 2300
 
2.7%
영 2287
 
2.6%
관 1991
 
2.3%
리 1903
 
2.2%
Other values (480) 57470
66.4%
Latin
ValueCountFrequency (%)
I 25
14.5%
T 19
 
11.0%
C 16
 
9.2%
G 12
 
6.9%
S 10
 
5.8%
A 9
 
5.2%
M 8
 
4.6%
D 8
 
4.6%
a 8
 
4.6%
P 6
 
3.5%
Other values (18) 52
30.1%
Common
ValueCountFrequency (%)
12468
82.1%
) 1026
 
6.8%
( 1026
 
6.8%
6 134
 
0.9%
- 132
 
0.9%
? 128
 
0.8%
, 85
 
0.6%
1 52
 
0.3%
0 25
 
0.2%
7 24
 
0.2%
Other values (12) 88
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 86586
84.9%
ASCII 15353
 
15.1%
None 8
 
< 0.1%
Compat Jamo 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12468
81.2%
) 1026
 
6.7%
( 1026
 
6.7%
6 134
 
0.9%
- 132
 
0.9%
? 128
 
0.8%
, 85
 
0.6%
1 52
 
0.3%
I 25
 
0.2%
0 25
 
0.2%
Other values (38) 252
 
1.6%
Hangul
ValueCountFrequency (%)
비 4588
 
5.3%
기 4002
 
4.6%
경 3634
 
4.2%
본 3362
 
3.9%
지 2720
 
3.1%
원 2336
 
2.7%
운 2300
 
2.7%
영 2287
 
2.6%
관 1991
 
2.3%
리 1903
 
2.2%
Other values (479) 57463
66.4%
Compat Jamo
ValueCountFrequency (%)
ㆍ 7
100.0%
None
ValueCountFrequency (%)
」 4
50.0%
「 4
50.0%

부서코드
Real number (ℝ)

Distinct945
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1589570.7
Minimum1003002
Maximum5500094
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:47:01.341031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1003002
5-th percentile1019043
Q11067040
median1240040
Q31400010
95-th percentile4600017
Maximum5500094
Range4497092
Interquartile range (IQR)332970

Descriptive statistics

Standard deviation1074247.4
Coefficient of variation (CV)0.67580977
Kurtosis4.3594995
Mean1589570.7
Median Absolute Deviation (MAD)167030
Skewness2.4020228
Sum1.5895707 × 1010
Variance1.1540075 × 1012
MonotonicityNot monotonic
2024-05-11T09:47:01.915654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300010 114
 
1.1%
1365020 90
 
0.9%
1280010 81
 
0.8%
1275010 76
 
0.8%
1043048 72
 
0.7%
1330010 71
 
0.7%
1270010 71
 
0.7%
1031010 68
 
0.7%
1325010 67
 
0.7%
1019010 64
 
0.6%
Other values (935) 9226
92.3%
ValueCountFrequency (%)
1003002 25
0.2%
1003004 13
 
0.1%
1003005 7
 
0.1%
1003006 33
0.3%
1004007 6
 
0.1%
1004015 8
 
0.1%
1004018 5
 
0.1%
1004020 19
0.2%
1004021 2
 
< 0.1%
1005002 12
 
0.1%
ValueCountFrequency (%)
5500094 3
 
< 0.1%
5500088 7
0.1%
5500083 5
0.1%
5500080 1
 
< 0.1%
5500077 3
 
< 0.1%
5500073 3
 
< 0.1%
5500041 2
 
< 0.1%
5500037 10
0.1%
5500034 1
 
< 0.1%
5500030 1
 
< 0.1%

부서명
Text

Distinct945
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T09:47:02.458364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length19
Mean length12.6125
Min length6

Characters and Unicode

Total characters126125
Distinct characters298
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)1.5%

Sample

1st row재난안전관리실 재난안전정책과
2nd row도시공간본부 도시계획상임기획과
3rd row강북소방서 현장대응단
4th row영등포소방서 대림119안전센터
5th row디자인정책관 도시경관담당관
ValueCountFrequency (%)
소방행정과 590
 
2.8%
총무과 534
 
2.5%
미래한강본부 342
 
1.6%
현장대응단 332
 
1.6%
의회사무처 244
 
1.2%
주택정책실 240
 
1.1%
도시기반시설본부 233
 
1.1%
원무과 228
 
1.1%
행정국 213
 
1.0%
경제정책실 208
 
1.0%
Other values (693) 17936
85.0%
2024-05-11T09:47:03.693789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11100
 
8.8%
과 6833
 
5.4%
서 3831
 
3.0%
소 3719
 
2.9%
정 3609
 
2.9%
관 3198
 
2.5%
방 3191
 
2.5%
원 2752
 
2.2%
부 2314
 
1.8%
지 2208
 
1.8%
Other values (288) 83370
66.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111865
88.7%
Space Separator 11100
 
8.8%
Decimal Number 3144
 
2.5%
Other Punctuation 16
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 6833
 
6.1%
서 3831
 
3.4%
소 3719
 
3.3%
정 3609
 
3.2%
관 3198
 
2.9%
방 3191
 
2.9%
원 2752
 
2.5%
부 2314
 
2.1%
지 2208
 
2.0%
시 2120
 
1.9%
Other values (281) 78090
69.8%
Decimal Number
ValueCountFrequency (%)
1 2091
66.5%
9 1019
32.4%
3 14
 
0.4%
8 14
 
0.4%
2 6
 
0.2%
Space Separator
ValueCountFrequency (%)
11100
100.0%
Other Punctuation
ValueCountFrequency (%)
? 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 111865
88.7%
Common 14260
 
11.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 6833
 
6.1%
서 3831
 
3.4%
소 3719
 
3.3%
정 3609
 
3.2%
관 3198
 
2.9%
방 3191
 
2.9%
원 2752
 
2.5%
부 2314
 
2.1%
지 2208
 
2.0%
시 2120
 
1.9%
Other values (281) 78090
69.8%
Common
ValueCountFrequency (%)
11100
77.8%
1 2091
 
14.7%
9 1019
 
7.1%
? 16
 
0.1%
3 14
 
0.1%
8 14
 
0.1%
2 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 111865
88.7%
ASCII 14260
 
11.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11100
77.8%
1 2091
 
14.7%
9 1019
 
7.1%
? 16
 
0.1%
3 14
 
0.1%
8 14
 
0.1%
2 6
 
< 0.1%
Hangul
ValueCountFrequency (%)
과 6833
 
6.1%
서 3831
 
3.4%
소 3719
 
3.3%
정 3609
 
3.2%
관 3198
 
2.9%
방 3191
 
2.9%
원 2752
 
2.5%
부 2314
 
2.1%
지 2208
 
2.0%
시 2120
 
1.9%
Other values (281) 78090
69.8%

목세목코드
Real number (ℝ)

Distinct82
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22195.797
Minimum10101
Maximum70301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:47:04.197603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10101
5-th percentile10104
Q120101
median20102
Q320304
95-th percentile40101
Maximum70301
Range60200
Interquartile range (IQR)203

Descriptive statistics

Standard deviation7133.1703
Coefficient of variation (CV)0.32137482
Kurtosis3.7009414
Mean22195.797
Median Absolute Deviation (MAD)99
Skewness1.3968979
Sum2.2195797 × 108
Variance50882118
MonotonicityNot monotonic
2024-05-11T09:47:04.648872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20101 3117
31.2%
20102 1290
12.9%
20303 842
 
8.4%
20201 724
 
7.2%
20304 508
 
5.1%
10104 473
 
4.7%
40101 464
 
4.6%
20301 221
 
2.2%
30101 212
 
2.1%
30711 165
 
1.7%
Other values (72) 1984
19.8%
ValueCountFrequency (%)
10101 139
 
1.4%
10102 150
 
1.5%
10103 69
 
0.7%
10104 473
 
4.7%
20101 3117
31.2%
20102 1290
12.9%
20103 36
 
0.4%
20104 16
 
0.2%
20201 724
 
7.2%
20202 3
 
< 0.1%
ValueCountFrequency (%)
70301 2
 
< 0.1%
70201 4
 
< 0.1%
70102 2
 
< 0.1%
70101 1
 
< 0.1%
60104 9
0.1%
50201 5
 
0.1%
50102 1
 
< 0.1%
50101 15
0.1%
40601 1
 
< 0.1%
40502 4
 
< 0.1%

목세목명
Text

Distinct82
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T09:47:05.127540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length6.6354
Min length2

Characters and Unicode

Total characters66354
Distinct characters128
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st row사무관리비
2nd row사무관리비
3rd row국내여비
4th row사무관리비
5th row사무관리비
ValueCountFrequency (%)
사무관리비 3117
30.9%
공공운영비 1290
12.8%
시책추진업무추진비 842
 
8.3%
국내여비 724
 
7.2%
부서운영업무추진비 508
 
5.0%
기간제근로자등보수 473
 
4.7%
시설비 464
 
4.6%
기관운영업무추진비 221
 
2.2%
사회보장적수혜금(국고보조재원 212
 
2.1%
사회복지사업보조 165
 
1.6%
Other values (76) 2085
20.6%
2024-05-11T09:47:06.130240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 8051
 
12.1%
무 5153
 
7.8%
사 4266
 
6.4%
관 3407
 
5.1%
리 3127
 
4.7%
공 2795
 
4.2%
진 2479
 
3.7%
추 2479
 
3.7%
운 2255
 
3.4%
영 2253
 
3.4%
Other values (118) 30089
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65168
98.2%
Open Punctuation 541
 
0.8%
Close Punctuation 415
 
0.6%
Other Punctuation 129
 
0.2%
Space Separator 101
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 8051
 
12.4%
무 5153
 
7.9%
사 4266
 
6.5%
관 3407
 
5.2%
리 3127
 
4.8%
공 2795
 
4.3%
진 2479
 
3.8%
추 2479
 
3.8%
운 2255
 
3.5%
영 2253
 
3.5%
Other values (113) 28903
44.4%
Other Punctuation
ValueCountFrequency (%)
, 127
98.4%
? 2
 
1.6%
Open Punctuation
ValueCountFrequency (%)
( 541
100.0%
Close Punctuation
ValueCountFrequency (%)
) 415
100.0%
Space Separator
ValueCountFrequency (%)
101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65168
98.2%
Common 1186
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 8051
 
12.4%
무 5153
 
7.9%
사 4266
 
6.5%
관 3407
 
5.2%
리 3127
 
4.8%
공 2795
 
4.3%
진 2479
 
3.8%
추 2479
 
3.8%
운 2255
 
3.5%
영 2253
 
3.5%
Other values (113) 28903
44.4%
Common
ValueCountFrequency (%)
( 541
45.6%
) 415
35.0%
, 127
 
10.7%
101
 
8.5%
? 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65166
98.2%
ASCII 1186
 
1.8%
Compat Jamo 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 8051
 
12.4%
무 5153
 
7.9%
사 4266
 
6.5%
관 3407
 
5.2%
리 3127
 
4.8%
공 2795
 
4.3%
진 2479
 
3.8%
추 2479
 
3.8%
운 2255
 
3.5%
영 2253
 
3.5%
Other values (112) 28901
44.3%
ASCII
ValueCountFrequency (%)
( 541
45.6%
) 415
35.0%
, 127
 
10.7%
101
 
8.5%
? 2
 
0.2%
Compat Jamo
ValueCountFrequency (%)
ㆍ 2
100.0%

적요
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

지급처리일
Real number (ℝ)

Distinct75
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20240344
Minimum20240201
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:47:06.580035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20240201
5-th percentile20240207
Q120240229
median20240326
Q320240418
95-th percentile20240508
Maximum20240510
Range309
Interquartile range (IQR)189

Descriptive statistics

Standard deviation94.562707
Coefficient of variation (CV)4.6719911 × 10-6
Kurtosis-1.1015642
Mean20240344
Median Absolute Deviation (MAD)93
Skewness0.030347142
Sum2.0240344 × 1011
Variance8942.1056
MonotonicityNot monotonic
2024-05-11T09:47:07.330789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20240320 246
 
2.5%
20240329 242
 
2.4%
20240409 237
 
2.4%
20240502 199
 
2.0%
20240429 193
 
1.9%
20240220 192
 
1.9%
20240419 192
 
1.9%
20240508 185
 
1.8%
20240422 182
 
1.8%
20240510 182
 
1.8%
Other values (65) 7950
79.5%
ValueCountFrequency (%)
20240201 38
 
0.4%
20240202 123
1.2%
20240205 118
1.2%
20240206 126
1.3%
20240207 160
1.6%
20240208 154
1.5%
20240213 144
1.4%
20240214 124
1.2%
20240215 152
1.5%
20240216 116
1.2%
ValueCountFrequency (%)
20240510 182
1.8%
20240509 161
1.6%
20240508 185
1.8%
20240507 143
1.4%
20240503 164
1.6%
20240502 199
2.0%
20240501 4
 
< 0.1%
20240430 171
1.7%
20240429 193
1.9%
20240426 145
1.5%

지급금액
Real number (ℝ)

SKEWED 

Distinct6403
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6428591 × 108
Minimum-1.5 × 108
Maximum2.674369 × 1011
Zeros2
Zeros (%)< 0.1%
Negative131
Negative (%)1.3%
Memory size166.0 KiB
2024-05-11T09:47:07.776900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.5 × 108
5-th percentile27000
Q1159000
median654500
Q33900000
95-th percentile1.1825132 × 108
Maximum2.674369 × 1011
Range2.675869 × 1011
Interquartile range (IQR)3741000

Descriptive statistics

Standard deviation4.0264189 × 109
Coefficient of variation (CV)24.508608
Kurtosis2499.3561
Mean1.6428591 × 108
Median Absolute Deviation (MAD)593415
Skewness46.153681
Sum1.6428591 × 1012
Variance1.6212049 × 1019
MonotonicityNot monotonic
2024-05-11T09:47:08.228072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 94
 
0.9%
200000 81
 
0.8%
50000 73
 
0.7%
300000 72
 
0.7%
400000 71
 
0.7%
80000 67
 
0.7%
20000 62
 
0.6%
24000 52
 
0.5%
800000 52
 
0.5%
1200000 51
 
0.5%
Other values (6393) 9325
93.2%
ValueCountFrequency (%)
-150000000 2
< 0.1%
-103812040 1
< 0.1%
-100000000 1
< 0.1%
-68020000 1
< 0.1%
-38300000 1
< 0.1%
-12382830 1
< 0.1%
-7470880 1
< 0.1%
-6608120 1
< 0.1%
-6359570 1
< 0.1%
-6333030 1
< 0.1%
ValueCountFrequency (%)
267436902000 1
< 0.1%
150468000000 1
< 0.1%
146703720000 1
< 0.1%
131844000000 1
< 0.1%
109291365000 1
< 0.1%
66049280000 1
< 0.1%
66040480000 1
< 0.1%
63750000000 1
< 0.1%
27697110000 1
< 0.1%
22820057000 1
< 0.1%

분야코드
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean380.394
Minimum10
Maximum900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:47:08.631736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q170
median120
Q3900
95-th percentile900
Maximum900
Range890
Interquartile range (IQR)830

Descriptive statistics

Standard deviation403.03033
Coefficient of variation (CV)1.0595076
Kurtosis-1.7210519
Mean380.394
Median Absolute Deviation (MAD)100
Skewness0.49886609
Sum3803940
Variance162433.45
MonotonicityNot monotonic
2024-05-11T09:47:09.023064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
900 3739
37.4%
80 1346
 
13.5%
10 1022
 
10.2%
20 887
 
8.9%
140 750
 
7.5%
120 570
 
5.7%
60 522
 
5.2%
70 476
 
4.8%
90 362
 
3.6%
110 163
 
1.6%
Other values (3) 163
 
1.6%
ValueCountFrequency (%)
10 1022
10.2%
20 887
8.9%
50 53
 
0.5%
60 522
 
5.2%
70 476
 
4.8%
80 1346
13.5%
90 362
 
3.6%
100 90
 
0.9%
110 163
 
1.6%
120 570
5.7%
ValueCountFrequency (%)
900 3739
37.4%
150 20
 
0.2%
140 750
 
7.5%
120 570
 
5.7%
110 163
 
1.6%
100 90
 
0.9%
90 362
 
3.6%
80 1346
 
13.5%
70 476
 
4.8%
60 522
 
5.2%

분야명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
3739 
사회복지
1346 
일반공공행정
1022 
공공질서및안전
887 
국토및지역개발
750 
Other values (8)
2256 

Length

Max length11
Median length7
Mean length4.0108
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 3739
37.4%
사회복지 1346
 
13.5%
일반공공행정 1022
 
10.2%
공공질서및안전 887
 
8.9%
국토및지역개발 750
 
7.5%
교통및물류 570
 
5.7%
문화및관광 522
 
5.2%
환경 476
 
4.8%
보건 362
 
3.6%
산업ㆍ중소기업및에너지 163
 
1.6%
Other values (3) 163
 
1.6%

Length

2024-05-11T09:47:09.418628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 3739
37.4%
사회복지 1346
 
13.5%
일반공공행정 1022
 
10.2%
공공질서및안전 887
 
8.9%
국토및지역개발 750
 
7.5%
교통및물류 570
 
5.7%
문화및관광 522
 
5.2%
환경 476
 
4.8%
보건 362
 
3.6%
산업ㆍ중소기업및에너지 163
 
1.6%
Other values (3) 163
 
1.6%

부문코드
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean383.1438
Minimum11
Maximum901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:47:09.815881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile16
Q171
median121
Q3901
95-th percentile901
Maximum901
Range890
Interquartile range (IQR)830

Descriptive statistics

Standard deviation401.61027
Coefficient of variation (CV)1.0481972
Kurtosis-1.7213756
Mean383.1438
Median Absolute Deviation (MAD)95
Skewness0.49982259
Sum3831438
Variance161290.81
MonotonicityNot monotonic
2024-05-11T09:47:10.276653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
901 3739
37.4%
16 810
 
8.1%
26 733
 
7.3%
142 727
 
7.3%
84 390
 
3.9%
82 379
 
3.8%
121 377
 
3.8%
86 329
 
3.3%
91 325
 
3.2%
61 267
 
2.7%
Other values (28) 1924
19.2%
ValueCountFrequency (%)
11 181
 
1.8%
14 31
 
0.3%
16 810
8.1%
23 20
 
0.2%
25 134
 
1.3%
26 733
7.3%
51 38
 
0.4%
53 15
 
0.1%
61 267
 
2.7%
62 29
 
0.3%
ValueCountFrequency (%)
901 3739
37.4%
153 20
 
0.2%
142 727
 
7.3%
141 23
 
0.2%
126 145
 
1.5%
123 48
 
0.5%
121 377
 
3.8%
116 28
 
0.3%
114 108
 
1.1%
113 25
 
0.2%

부문명
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
3739 
일반행정
810 
소방
733 
지역및도시
727 
보육ㆍ가족및여성
390 
Other values (33)
3601 

Length

Max length10
Median length2
Mean length3.5826
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 3739
37.4%
일반행정 810
 
8.1%
소방 733
 
7.3%
지역및도시 727
 
7.3%
보육ㆍ가족및여성 390
 
3.9%
취약계층지원 379
 
3.8%
도로 377
 
3.8%
노동 329
 
3.3%
보건의료 325
 
3.2%
문화예술 267
 
2.7%
Other values (28) 1924
19.2%

Length

2024-05-11T09:47:10.701562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 3739
37.4%
일반행정 810
 
8.1%
소방 733
 
7.3%
지역및도시 727
 
7.3%
보육ㆍ가족및여성 390
 
3.9%
취약계층지원 379
 
3.8%
도로 377
 
3.8%
노동 329
 
3.3%
보건의료 325
 
3.2%
문화예술 267
 
2.7%
Other values (28) 1924
19.2%

Interactions

2024-05-11T09:46:53.681105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:43.523631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:45.772300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:48.155119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:50.339811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:52.059913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:53.945692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:43.805598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:46.154822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:48.510194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:50.652499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:52.347692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:54.225636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:44.208329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:46.581469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:48.887471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:50.942678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:52.623079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:54.530348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:44.632705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:46.879574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:49.310367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:51.254530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:52.903299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:54.834086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:44.911167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:47.278927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:49.692388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:51.531165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:53.174951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:55.097495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:45.508904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:47.673790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:50.019635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:51.793460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:46:53.428094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T09:47:10.984648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부서코드목세목코드목세목명지급처리일지급금액분야코드분야명부문코드부문명
부서코드1.0000.5610.6320.0650.0000.3570.4780.3570.591
목세목코드0.5611.0001.0000.0860.4100.4650.5190.4650.768
목세목명0.6321.0001.0000.2480.7810.8130.8010.8130.897
지급처리일0.0650.0860.2481.0000.0440.1430.0850.1430.161
지급금액0.0000.4100.7810.0441.0000.0000.1140.0000.207
분야코드0.3570.4650.8130.1430.0001.0001.0001.0001.000
분야명0.4780.5190.8010.0850.1141.0001.0001.0001.000
부문코드0.3570.4650.8130.1430.0001.0001.0001.0001.000
부문명0.5910.7680.8970.1610.2071.0001.0001.0001.000
2024-05-11T09:47:11.359267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부문명분야명
부문명1.0000.999
분야명0.9991.000
2024-05-11T09:47:11.612497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부서코드목세목코드지급처리일지급금액분야코드부문코드분야명부문명
부서코드1.0000.1180.0410.042-0.003-0.0120.2390.272
목세목코드0.1181.0000.0370.147-0.159-0.1740.2630.418
지급처리일0.0410.0371.0000.059-0.054-0.0530.0430.071
지급금액0.0420.1470.0591.000-0.249-0.2510.0560.091
분야코드-0.003-0.159-0.054-0.2491.0000.9980.9990.998
부문코드-0.012-0.174-0.053-0.2510.9981.0000.9990.998
분야명0.2390.2630.0430.0560.9990.9991.0000.999
부문명0.2720.4180.0710.0910.9980.9980.9991.000

Missing values

2024-05-11T09:46:55.490858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T09:46:56.113184image/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

회계년도세부사업코드세부사업명부서코드부서명목세목코드목세목명적요지급처리일지급금액분야코드분야명부문코드부문명
3591420246110000200831489기본경비1079020재난안전관리실 재난안전정책과20101사무관리비<NA>202403184140800900기타901기타
279452024611000020213057A기본경비1067058도시공간본부 도시계획상임기획과20101사무관리비<NA>2024032825000900기타901기타
28328202461100002016304C8기본경비1079095강북소방서 현장대응단20201국내여비<NA>2024032820000900기타901기타
24824202461100002016304C8기본경비1400090영등포소방서 대림119안전센터20101사무관리비<NA>2024040380000900기타901기타
57710202461100002015305EE기본경비1067056디자인정책관 도시경관담당관20101사무관리비<NA>20240207736000900기타901기타
4476120246110000200830680시정보도 간행물 구독 및 언론소통 강화1013010대변인 언론담당관20402특정업무경비<NA>20240229528610010일반공공행정16일반행정
16920202461100002016304F8기본경비1079108119특수구조단 산악구조대20101사무관리비<NA>20240416414000900기타901기타
412320246110000200830008기본경비1022040시민감사옴부즈만위원회20101사무관리비<NA>2024050749500900기타901기타
844320246110000201930149종교계와 함께하는 문화행사 지원1043010문화본부 문화정책과30704민간행사사업보조<NA>202404295400000060문화및관광61문화예술
5650920246110000200832544가정위탁아동 지원3900081강북구 복지국 청소년과30102사회보장적수혜금(취약계층,지방재원<NA>2024020890000080사회복지84보육ㆍ가족및여성
회계년도세부사업코드세부사업명부서코드부서명목세목코드목세목명적요지급처리일지급금액분야코드분야명부문코드부문명
143972024611000020163057F백남준 기념관 운영1043054시립미술관 북서울미술관 학예과20101사무관리비<NA>202404191650060문화및관광61문화예술
5890320246110000200831830기본경비(도시철도)1196013도시기반시설본부 도시철도국 영동대로복합개발추진단20101사무관리비<NA>202402051920000900기타901기타
2151420246110000200831299서울 동행일자리1300010서울역사박물관 총무과10104기간제근로자등보수<NA>20240409516681080사회복지86노동
3325220246110000202130670입양대상아동 보호비 지원4800068금천구 복지가족국 아동청소년과30711사회복지사업보조<NA>20240320200000080사회복지84보육ㆍ가족및여성
5852320246110000200831190기본경비1037010복지정책실 복지정책과20101사무관리비<NA>202402061358800900기타901기타
418362024611000020143002A서울, 초록 물들이기 프로젝트1049030푸른도시여가국 조경과20303시책추진업무추진비<NA>20240307131000140국토및지역개발142지역및도시
49665202461100002016304C8기본경비1410010도봉소방서 소방행정과20102공공운영비<NA>20240222648540900기타901기타
30151202461100002016304C8기본경비1079084서초소방서 현장대응단20201국내여비<NA>2024032687500900기타901기타
3099120246110000201430319서울형 뉴딜일자리3300078용산구 생활지원국 어르신청소년과10104기간제근로자등보수<NA>2024032530000080사회복지86노동
4475120246110000200832130시청사 청소관리1031010행정국 총무과10104기간제근로자등보수<NA>202402291271593010일반공공행정16일반행정