Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows963
Duplicate rows (%)9.6%
Total size in memory1.2 MiB
Average record size in memory121.0 B

Variable types

Categorical5
Text9

Dataset

Description부산광역시 상수도본부 주관부서월간집계정보입니다. 해당부서에서 신청한 예산에 대해 승인된 예산들로 된 월간집계정보테이블로 주관부서년간집계테이블의 기초자료입니다.
Author부산광역시
URLhttps://www.data.go.kr/data/15083541/fileData.do

Alerts

계획변경,취소 has constant value ""Constant
Dataset has 963 (9.6%) duplicate rowsDuplicates
본예산이체 is highly overall correlated with 예산년월 and 1 other fieldsHigh correlation
이월예산이체 is highly overall correlated with 예산년월 and 1 other fieldsHigh correlation
예산년월 is highly overall correlated with 본예산이체 and 1 other fieldsHigh correlation
예산종류 is highly imbalanced (55.1%)Imbalance

Reproduction

Analysis started2023-12-12 06:22:30.147180
Analysis finished2023-12-12 06:22:31.390416
Duration1.24 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

예산종류
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8101 
2
1888 
3
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8101
81.0%
2 1888
 
18.9%
3 11
 
0.1%

Length

2023-12-12T15:22:31.457902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:22:31.563052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8101
81.0%
2 1888
 
18.9%
3 11
 
0.1%

예산년월
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020-01
1156 
2020-03
1086 
2020-12
1021 
2020-06
817 
2020-07
797 
Other values (7)
5123 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12
2nd row2020-05
3rd row2020-01
4th row2020-08
5th row2020-12

Common Values

ValueCountFrequency (%)
2020-01 1156
11.6%
2020-03 1086
10.9%
2020-12 1021
10.2%
2020-06 817
8.2%
2020-07 797
8.0%
2020-09 786
7.9%
2020-05 776
7.8%
2020-02 755
7.5%
2020-11 749
7.5%
2020-10 720
7.2%
Other values (2) 1337
13.4%

Length

2023-12-12T15:22:31.678088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-01 1156
11.6%
2020-03 1086
10.9%
2020-12 1021
10.2%
2020-06 817
8.2%
2020-07 797
8.0%
2020-09 786
7.9%
2020-05 776
7.8%
2020-02 755
7.5%
2020-11 749
7.5%
2020-10 720
7.2%
Other values (2) 1337
13.4%
Distinct160
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:22:32.044704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters50000
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

Unique14 ?
Unique (%)0.1%

Sample

1st row22142
2nd row22152
3rd row12459
4th row12433
5th row12211
ValueCountFrequency (%)
22142 885
 
8.8%
12559 732
 
7.3%
22152 526
 
5.3%
12259 403
 
4.0%
12511 384
 
3.8%
12503 295
 
2.9%
12524 257
 
2.6%
12523 251
 
2.5%
12552 222
 
2.2%
22176 201
 
2.0%
Other values (150) 5844
58.4%
2023-12-12T15:22:32.676022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 17065
34.1%
1 13671
27.3%
5 7560
15.1%
3 3691
 
7.4%
4 3268
 
6.5%
9 1604
 
3.2%
6 1156
 
2.3%
0 953
 
1.9%
7 554
 
1.1%
8 181
 
0.4%
Other values (7) 297
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49703
99.4%
Uppercase Letter 297
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 17065
34.3%
1 13671
27.5%
5 7560
15.2%
3 3691
 
7.4%
4 3268
 
6.6%
9 1604
 
3.2%
6 1156
 
2.3%
0 953
 
1.9%
7 554
 
1.1%
8 181
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
B 151
50.8%
F 85
28.6%
E 22
 
7.4%
G 12
 
4.0%
H 12
 
4.0%
A 12
 
4.0%
I 3
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49703
99.4%
Latin 297
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 17065
34.3%
1 13671
27.5%
5 7560
15.2%
3 3691
 
7.4%
4 3268
 
6.6%
9 1604
 
3.2%
6 1156
 
2.3%
0 953
 
1.9%
7 554
 
1.1%
8 181
 
0.4%
Latin
ValueCountFrequency (%)
B 151
50.8%
F 85
28.6%
E 22
 
7.4%
G 12
 
4.0%
H 12
 
4.0%
A 12
 
4.0%
I 3
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 17065
34.1%
1 13671
27.3%
5 7560
15.1%
3 3691
 
7.4%
4 3268
 
6.5%
9 1604
 
3.2%
6 1156
 
2.3%
0 953
 
1.9%
7 554
 
1.1%
8 181
 
0.4%
Other values (7) 297
 
0.6%
Distinct786
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:22:33.178699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9993
Min length3

Characters and Unicode

Total characters39993
Distinct characters15
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

Unique49 ?
Unique (%)0.5%

Sample

1st rowB801
2nd rowB704
3rd row8233
4th row0482
5th row5372
ValueCountFrequency (%)
0015 141
 
1.4%
0279 131
 
1.3%
0502 131
 
1.3%
0904 131
 
1.3%
0501 130
 
1.3%
0345 130
 
1.3%
8140 130
 
1.3%
0675 129
 
1.3%
0026 128
 
1.3%
0690 128
 
1.3%
Other values (772) 8691
86.9%
2023-12-12T15:22:33.846934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10297
25.7%
3 3991
 
10.0%
1 3967
 
9.9%
8 3871
 
9.7%
5 3791
 
9.5%
2 3548
 
8.9%
4 2449
 
6.1%
6 2427
 
6.1%
9 2096
 
5.2%
7 1883
 
4.7%
Other values (5) 1673
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38320
95.8%
Uppercase Letter 1435
 
3.6%
Lowercase Letter 236
 
0.6%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10297
26.9%
3 3991
 
10.4%
1 3967
 
10.4%
8 3871
 
10.1%
5 3791
 
9.9%
2 3548
 
9.3%
4 2449
 
6.4%
6 2427
 
6.3%
9 2096
 
5.5%
7 1883
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
B 904
63.0%
C 282
 
19.7%
A 249
 
17.4%
Lowercase Letter
ValueCountFrequency (%)
c 236
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38322
95.8%
Latin 1671
 
4.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10297
26.9%
3 3991
 
10.4%
1 3967
 
10.4%
8 3871
 
10.1%
5 3791
 
9.9%
2 3548
 
9.3%
4 2449
 
6.4%
6 2427
 
6.3%
9 2096
 
5.5%
7 1883
 
4.9%
Latin
ValueCountFrequency (%)
B 904
54.1%
C 282
 
16.9%
A 249
 
14.9%
c 236
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10297
25.7%
3 3991
 
10.0%
1 3967
 
9.9%
8 3871
 
9.7%
5 3791
 
9.5%
2 3548
 
8.9%
4 2449
 
6.1%
6 2427
 
6.1%
9 2096
 
5.2%
7 1883
 
4.7%
Other values (5) 1673
 
4.2%
Distinct98
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:22:34.149467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length5.4486
Min length2

Characters and Unicode

Total characters54486
Distinct characters131
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.2%

Sample

1st row시설비
2nd row시설비
3rd row수선유지비
4th row월액여비
5th row무기계약근로자보수
ValueCountFrequency (%)
시설비 1564
15.6%
수선유지비 1439
 
14.3%
보수 664
 
6.6%
무기계약근로자보수 577
 
5.7%
공공운영비 504
 
5.0%
사무관리비 466
 
4.6%
사회보험부담금 345
 
3.4%
국내여비 290
 
2.9%
기타복리후생비 288
 
2.9%
일반재료비 264
 
2.6%
Other values (90) 3653
36.3%
2023-12-12T15:22:34.599335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7831
 
14.4%
4085
 
7.5%
2354
 
4.3%
2059
 
3.8%
1736
 
3.2%
1714
 
3.1%
1623
 
3.0%
1562
 
2.9%
1519
 
2.8%
1508
 
2.8%
Other values (121) 28495
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54247
99.6%
Dash Punctuation 178
 
0.3%
Space Separator 54
 
0.1%
Other Punctuation 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7831
 
14.4%
4085
 
7.5%
2354
 
4.3%
2059
 
3.8%
1736
 
3.2%
1714
 
3.2%
1623
 
3.0%
1562
 
2.9%
1519
 
2.8%
1508
 
2.8%
Other values (118) 28256
52.1%
Dash Punctuation
ValueCountFrequency (%)
- 178
100.0%
Space Separator
ValueCountFrequency (%)
54
100.0%
Other Punctuation
ValueCountFrequency (%)
· 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54247
99.6%
Common 239
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
7831
 
14.4%
4085
 
7.5%
2354
 
4.3%
2059
 
3.8%
1736
 
3.2%
1714
 
3.2%
1623
 
3.0%
1562
 
2.9%
1519
 
2.8%
1508
 
2.8%
Other values (118) 28256
52.1%
Common
ValueCountFrequency (%)
- 178
74.5%
54
 
22.6%
· 7
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 54247
99.6%
ASCII 232
 
0.4%
None 7
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
7831
 
14.4%
4085
 
7.5%
2354
 
4.3%
2059
 
3.8%
1736
 
3.2%
1714
 
3.2%
1623
 
3.0%
1562
 
2.9%
1519
 
2.8%
1508
 
2.8%
Other values (118) 28256
52.1%
ASCII
ValueCountFrequency (%)
- 178
76.7%
54
 
23.3%
None
ValueCountFrequency (%)
· 7
100.0%
Distinct695
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:22:34.925483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length31
Mean length10.786
Min length2

Characters and Unicode

Total characters107860
Distinct characters423
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.6%

Sample

1st row청학동 신한기공사~미창석유 구경확대공사(1공구)
2nd row블록유량계 교체
3rd row콜센터시스템 유지관리
4th row월액여비
5th row실무사무원(현장)
ValueCountFrequency (%)
807
 
3.8%
교체 535
 
2.5%
주변 409
 
1.9%
유지관리 314
 
1.5%
수당 294
 
1.4%
일반수용비 267
 
1.2%
직급보조비 258
 
1.2%
공공요금 258
 
1.2%
제세 258
 
1.2%
정액급식비 257
 
1.2%
Other values (1111) 17804
83.0%
2023-12-12T15:22:35.404687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11491
 
10.7%
5206
 
4.8%
4916
 
4.6%
2479
 
2.3%
2471
 
2.3%
2334
 
2.2%
2160
 
2.0%
2154
 
2.0%
1845
 
1.7%
1759
 
1.6%
Other values (413) 71045
65.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 92186
85.5%
Space Separator 11491
 
10.7%
Close Punctuation 1214
 
1.1%
Open Punctuation 1214
 
1.1%
Decimal Number 889
 
0.8%
Other Punctuation 370
 
0.3%
Uppercase Letter 311
 
0.3%
Math Symbol 119
 
0.1%
Dash Punctuation 55
 
0.1%
Control 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5206
 
5.6%
4916
 
5.3%
2479
 
2.7%
2471
 
2.7%
2334
 
2.5%
2160
 
2.3%
2154
 
2.3%
1845
 
2.0%
1759
 
1.9%
1668
 
1.8%
Other values (374) 65194
70.7%
Uppercase Letter
ValueCountFrequency (%)
C 73
23.5%
T 47
15.1%
S 36
11.6%
V 34
10.9%
A 18
 
5.8%
E 15
 
4.8%
I 12
 
3.9%
M 12
 
3.9%
L 12
 
3.9%
P 10
 
3.2%
Other values (8) 42
13.5%
Decimal Number
ValueCountFrequency (%)
1 271
30.5%
2 270
30.4%
3 127
14.3%
5 76
 
8.5%
4 63
 
7.1%
7 31
 
3.5%
0 25
 
2.8%
8 13
 
1.5%
6 11
 
1.2%
9 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
· 227
61.4%
, 141
38.1%
. 2
 
0.5%
Math Symbol
ValueCountFrequency (%)
~ 102
85.7%
17
 
14.3%
Space Separator
ValueCountFrequency (%)
11491
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1214
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1214
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%
Control
ValueCountFrequency (%)
6
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 92186
85.5%
Common 15358
 
14.2%
Latin 316
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
5206
 
5.6%
4916
 
5.3%
2479
 
2.7%
2471
 
2.7%
2334
 
2.5%
2160
 
2.3%
2154
 
2.3%
1845
 
2.0%
1759
 
1.9%
1668
 
1.8%
Other values (374) 65194
70.7%
Common
ValueCountFrequency (%)
11491
74.8%
) 1214
 
7.9%
( 1214
 
7.9%
1 271
 
1.8%
2 270
 
1.8%
· 227
 
1.5%
, 141
 
0.9%
3 127
 
0.8%
~ 102
 
0.7%
5 76
 
0.5%
Other values (10) 225
 
1.5%
Latin
ValueCountFrequency (%)
C 73
23.1%
T 47
14.9%
S 36
11.4%
V 34
10.8%
A 18
 
5.7%
E 15
 
4.7%
I 12
 
3.8%
M 12
 
3.8%
L 12
 
3.8%
P 10
 
3.2%
Other values (9) 47
14.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 92186
85.5%
ASCII 15430
 
14.3%
None 227
 
0.2%
Math Operators 17
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11491
74.5%
) 1214
 
7.9%
( 1214
 
7.9%
1 271
 
1.8%
2 270
 
1.7%
, 141
 
0.9%
3 127
 
0.8%
~ 102
 
0.7%
5 76
 
0.5%
C 73
 
0.5%
Other values (27) 451
 
2.9%
Hangul
ValueCountFrequency (%)
5206
 
5.6%
4916
 
5.3%
2479
 
2.7%
2471
 
2.7%
2334
 
2.5%
2160
 
2.3%
2154
 
2.3%
1845
 
2.0%
1759
 
1.9%
1668
 
1.8%
Other values (374) 65194
70.7%
None
ValueCountFrequency (%)
· 227
100.0%
Math Operators
ValueCountFrequency (%)
17
100.0%
Distinct1693
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:22:35.669919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length1
Mean length3.9087
Min length1

Characters and Unicode

Total characters39087
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

Unique1251 ?
Unique (%)12.5%

Sample

1st row-8158000
2nd row-
3rd row12600000
4th row-
5th row-26000000
ValueCountFrequency (%)
5702
57.0%
0 675
 
6.8%
20000000 50
 
0.5%
450000 44
 
0.4%
10000000 44
 
0.4%
6000000 41
 
0.4%
100000000 37
 
0.4%
90000000 36
 
0.4%
3000000 35
 
0.4%
150000000 33
 
0.3%
Other values (1575) 3303
33.0%
2023-12-12T15:22:36.061226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18888
48.3%
- 6134
 
15.7%
4298
 
11.0%
1 1767
 
4.5%
2 1445
 
3.7%
5 1276
 
3.3%
4 1093
 
2.8%
3 1036
 
2.7%
6 929
 
2.4%
8 807
 
2.1%
Other values (2) 1414
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28655
73.3%
Dash Punctuation 6134
 
15.7%
Space Separator 4298
 
11.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18888
65.9%
1 1767
 
6.2%
2 1445
 
5.0%
5 1276
 
4.5%
4 1093
 
3.8%
3 1036
 
3.6%
6 929
 
3.2%
8 807
 
2.8%
7 722
 
2.5%
9 692
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 6134
100.0%
Space Separator
ValueCountFrequency (%)
4298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39087
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18888
48.3%
- 6134
 
15.7%
4298
 
11.0%
1 1767
 
4.5%
2 1445
 
3.7%
5 1276
 
3.3%
4 1093
 
2.8%
3 1036
 
2.7%
6 929
 
2.4%
8 807
 
2.1%
Other values (2) 1414
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39087
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18888
48.3%
- 6134
 
15.7%
4298
 
11.0%
1 1767
 
4.5%
2 1445
 
3.7%
5 1276
 
3.3%
4 1093
 
2.8%
3 1036
 
2.7%
6 929
 
2.4%
8 807
 
2.1%
Other values (2) 1414
 
3.6%
Distinct62
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:22:36.305287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length1
Mean length1.4773
Min length1

Characters and Unicode

Total characters14773
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

Unique60 ?
Unique (%)0.6%

Sample

1st row0
2nd row-
3rd row0
4th row-
5th row0
ValueCountFrequency (%)
5702
57.0%
0 4238
42.4%
18000000 1
 
< 0.1%
3750000 1
 
< 0.1%
68000000 1
 
< 0.1%
15331500 1
 
< 0.1%
21700000 1
 
< 0.1%
47695000 1
 
< 0.1%
256744000 1
 
< 0.1%
64806500 1
 
< 0.1%
Other values (52) 52
 
0.5%
2023-12-12T15:22:36.690571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 5702
38.6%
0 4445
30.1%
4298
29.1%
2 50
 
0.3%
1 42
 
0.3%
4 41
 
0.3%
3 38
 
0.3%
6 35
 
0.2%
9 33
 
0.2%
7 31
 
0.2%
Other values (2) 58
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 5702
38.6%
Decimal Number 4773
32.3%
Space Separator 4298
29.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4445
93.1%
2 50
 
1.0%
1 42
 
0.9%
4 41
 
0.9%
3 38
 
0.8%
6 35
 
0.7%
9 33
 
0.7%
7 31
 
0.6%
5 31
 
0.6%
8 27
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 5702
100.0%
Space Separator
ValueCountFrequency (%)
4298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14773
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 5702
38.6%
0 4445
30.1%
4298
29.1%
2 50
 
0.3%
1 42
 
0.3%
4 41
 
0.3%
3 38
 
0.3%
6 35
 
0.2%
9 33
 
0.2%
7 31
 
0.2%
Other values (2) 58
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 5702
38.6%
0 4445
30.1%
4298
29.1%
2 50
 
0.3%
1 42
 
0.3%
4 41
 
0.3%
3 38
 
0.3%
6 35
 
0.2%
9 33
 
0.2%
7 31
 
0.2%
Other values (2) 58
 
0.4%
Distinct426
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:22:36.901126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length1
Mean length1.8312
Min length1

Characters and Unicode

Total characters18312
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

Unique366 ?
Unique (%)3.7%

Sample

1st row-8158000
2nd row-
3rd row0
4th row-
5th row-26000000
ValueCountFrequency (%)
5702
57.0%
0 3762
37.6%
20000000 11
 
0.1%
90000000 9
 
0.1%
50000000 8
 
0.1%
300000000 7
 
0.1%
80000000 7
 
0.1%
150000000 7
 
0.1%
200000000 6
 
0.1%
100000000 6
 
0.1%
Other values (391) 475
 
4.8%
2023-12-12T15:22:37.247778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6380
34.8%
- 6009
32.8%
4298
23.5%
1 297
 
1.6%
5 225
 
1.2%
2 206
 
1.1%
3 178
 
1.0%
4 177
 
1.0%
8 149
 
0.8%
9 143
 
0.8%
Other values (2) 250
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8005
43.7%
Dash Punctuation 6009
32.8%
Space Separator 4298
23.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6380
79.7%
1 297
 
3.7%
5 225
 
2.8%
2 206
 
2.6%
3 178
 
2.2%
4 177
 
2.2%
8 149
 
1.9%
9 143
 
1.8%
6 131
 
1.6%
7 119
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 6009
100.0%
Space Separator
ValueCountFrequency (%)
4298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18312
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6380
34.8%
- 6009
32.8%
4298
23.5%
1 297
 
1.6%
5 225
 
1.2%
2 206
 
1.1%
3 178
 
1.0%
4 177
 
1.0%
8 149
 
0.8%
9 143
 
0.8%
Other values (2) 250
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6380
34.8%
- 6009
32.8%
4298
23.5%
1 297
 
1.6%
5 225
 
1.2%
2 206
 
1.1%
3 178
 
1.0%
4 177
 
1.0%
8 149
 
0.8%
9 143
 
0.8%
Other values (2) 250
 
1.4%
Distinct291
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:22:37.492967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length1
Mean length1.7065
Min length1

Characters and Unicode

Total characters17065
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

Unique238 ?
Unique (%)2.4%

Sample

1st row0
2nd row-
3rd row0
4th row-
5th row0
ValueCountFrequency (%)
5702
57.0%
0 3897
39.0%
60000 20
 
0.2%
50000000 12
 
0.1%
10000000 10
 
0.1%
20000000 9
 
0.1%
7000000 9
 
0.1%
3000000 9
 
0.1%
2000000 9
 
0.1%
800000 6
 
0.1%
Other values (214) 317
 
3.2%
2023-12-12T15:22:37.916619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 5881
34.5%
0 5804
34.0%
4298
25.2%
1 201
 
1.2%
2 167
 
1.0%
6 130
 
0.8%
5 124
 
0.7%
3 123
 
0.7%
4 96
 
0.6%
8 93
 
0.5%
Other values (2) 148
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6886
40.4%
Dash Punctuation 5881
34.5%
Space Separator 4298
25.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5804
84.3%
1 201
 
2.9%
2 167
 
2.4%
6 130
 
1.9%
5 124
 
1.8%
3 123
 
1.8%
4 96
 
1.4%
8 93
 
1.4%
7 91
 
1.3%
9 57
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 5881
100.0%
Space Separator
ValueCountFrequency (%)
4298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17065
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 5881
34.5%
0 5804
34.0%
4298
25.2%
1 201
 
1.2%
2 167
 
1.0%
6 130
 
0.8%
5 124
 
0.7%
3 123
 
0.7%
4 96
 
0.6%
8 93
 
0.5%
Other values (2) 148
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17065
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 5881
34.5%
0 5804
34.0%
4298
25.2%
1 201
 
1.2%
2 167
 
1.0%
6 130
 
0.8%
5 124
 
0.7%
3 123
 
0.7%
4 96
 
0.6%
8 93
 
0.5%
Other values (2) 148
 
0.9%

계획변경,취소
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 10000
100.0%

Length

2023-12-12T15:22:38.073413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:22:38.164898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10000
100.0%

본예산이체
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
-
5702 
0
4298 

Length

Max length2
Median length1
Mean length1.4298
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row-
3rd row0
4th row-
5th row0

Common Values

ValueCountFrequency (%)
- 5702
57.0%
0 4298
43.0%

Length

2023-12-12T15:22:38.296928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:22:38.410002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5702
57.0%
0 4298
43.0%

이월예산이체
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
-
5702 
0
4298 

Length

Max length2
Median length1
Mean length1.4298
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row-
3rd row0
4th row-
5th row0

Common Values

ValueCountFrequency (%)
- 5702
57.0%
0 4298
43.0%

Length

2023-12-12T15:22:38.531143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:22:38.662179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5702
57.0%
0 4298
43.0%
Distinct426
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:22:38.842592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length1
Mean length1.4572
Min length1

Characters and Unicode

Total characters14572
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

Unique366 ?
Unique (%)3.7%

Sample

1st row-8158000
2nd row-
3rd row-
4th row-
5th row-26000000
ValueCountFrequency (%)
9442
94.4%
0 22
 
0.2%
20000000 11
 
0.1%
90000000 9
 
0.1%
50000000 8
 
0.1%
300000000 7
 
0.1%
80000000 7
 
0.1%
150000000 7
 
0.1%
200000000 6
 
0.1%
100000000 6
 
0.1%
Other values (391) 475
 
4.8%
2023-12-12T15:22:39.244173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 9749
66.9%
0 2640
 
18.1%
558
 
3.8%
1 297
 
2.0%
5 225
 
1.5%
2 206
 
1.4%
3 178
 
1.2%
4 177
 
1.2%
8 149
 
1.0%
9 143
 
1.0%
Other values (2) 250
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 9749
66.9%
Decimal Number 4265
29.3%
Space Separator 558
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2640
61.9%
1 297
 
7.0%
5 225
 
5.3%
2 206
 
4.8%
3 178
 
4.2%
4 177
 
4.2%
8 149
 
3.5%
9 143
 
3.4%
6 131
 
3.1%
7 119
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 9749
100.0%
Space Separator
ValueCountFrequency (%)
558
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14572
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 9749
66.9%
0 2640
 
18.1%
558
 
3.8%
1 297
 
2.0%
5 225
 
1.5%
2 206
 
1.4%
3 178
 
1.2%
4 177
 
1.2%
8 149
 
1.0%
9 143
 
1.0%
Other values (2) 250
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14572
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 9749
66.9%
0 2640
 
18.1%
558
 
3.8%
1 297
 
2.0%
5 225
 
1.5%
2 206
 
1.4%
3 178
 
1.2%
4 177
 
1.2%
8 149
 
1.0%
9 143
 
1.0%
Other values (2) 250
 
1.7%

Correlations

2023-12-12T15:22:39.721054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예산종류예산년월예산과목명전월이월본예산이체이월예산이체
예산종류1.0000.3320.9990.1720.0230.023
예산년월0.3321.0000.2780.5850.9010.901
예산과목명0.9990.2781.0000.0000.2210.221
전월이월0.1720.5850.0001.0001.0001.000
본예산이체0.0230.9010.2211.0001.0001.000
이월예산이체0.0230.9010.2211.0001.0001.000
2023-12-12T15:22:39.833788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예산년월본예산이체예산종류이월예산이체
예산년월1.0000.7470.1590.747
본예산이체0.7471.0000.0381.000
예산종류0.1590.0381.0000.038
이월예산이체0.7471.0000.0381.000
2023-12-12T15:22:39.934673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예산종류예산년월본예산이체이월예산이체
예산종류1.0000.1590.0380.038
예산년월0.1591.0000.7470.747
본예산이체0.0380.7471.0001.000
이월예산이체0.0380.7471.0001.000

Missing values

2023-12-12T15:22:31.064561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:22:31.283038image/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

예산종류예산년월예산코드분류코드예산과목명분류코드명배정예산전월이월추경예산전용예산계획변경,취소본예산이체이월예산이체금월추경
1001322020-1222142B801시설비청학동 신한기공사~미창석유 구경확대공사(1공구)-81580000-81580000-00-8158000
925222020-0522152B704시설비블록유량계 교체--------
39512020-01124598233수선유지비콜센터시스템 유지관리12600000000-00-
514412020-08124330482월액여비월액여비--------
760812020-12122115372무기계약근로자보수실무사무원(현장)-260000000-260000000-00-26000000
776212020-12123140333직책급업무수행경비직책급업무수행경비0000-000
820412020-12125598140징수및수용가관리비-수선유지교체비-수선유지비누수수리 도급773580000077358000-00-
894122020-0322152c034시설비매리취수장 태양광발전장치 설치(이월)--------
667212020-10125240675공공운영비공공요금 및 제세--------
280812020-04125598200수선유지비급수불편해소--------
예산종류예산년월예산코드분류코드예산과목명분류코드명배정예산전월이월추경예산전용예산계획변경,취소본예산이체이월예산이체금월추경
674612020-10125530870기타복리후생비급량비--------
171412020-03123438060일반재료비소독제 및 시약등 구입6000000000-00-
466812020-07125240675공공운영비공공요금 및 제세--------
819612020-12125593996수선유지비블록 유수율 유지관리--------
368412020-06122598211수선유지비수질 TMS실 측정기기 위탁관리 용역0000-00-
179612020-0312448A401연구용역비수도정비기본계획 수립 및 정수장 기술진단 용역--------
37112020-01124400534시책업무추진비시책업무추진비3400000000-00-
254312020-04125115336무기계약근로자보수시설장비관리원--------
830522020-0122142A702시설비덕산∼화명 비상연계 노후관개량공사(4공구)7360000000000-00-
279712020-04125598143수선유지비블록감시설비 유지관리--------

Duplicate rows

Most frequently occurring

예산종류예산년월예산코드분류코드예산과목명분류코드명배정예산전월이월추경예산전용예산계획변경,취소본예산이체이월예산이체금월추경# duplicates
21712020-04125030015보수수당--------12
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