Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory43.3 B

Variable types

Categorical5

Alerts

일반회계 금액(백만원) is highly imbalanced (91.9%)Imbalance

Reproduction

Analysis started2023-12-10 11:44:25.420531
Analysis finished2023-12-10 11:44:27.855518
Duration2.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2008
34 
2009
34 
2010
32 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008
2nd row2008
3rd row2008
4th row2008
5th row2008

Common Values

ValueCountFrequency (%)
2008 34
34.0%
2009 34
34.0%
2010 32
32.0%

Length

2023-12-10T20:44:27.958833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:44:28.100925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2008 34
34.0%
2009 34
34.0%
2010 32
32.0%

행정구역명
Categorical

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경상북도
 
6
강원도
 
6
경상남도
 
6
울산광역시
 
6
세종특별자치시
 
6
Other values (12)
70 

Length

Max length7
Median length5
Mean length4.67
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도
2nd row강원도
3rd row울산광역시
4th row경상북도
5th row세종특별자치시

Common Values

ValueCountFrequency (%)
경상북도 6
 
6.0%
강원도 6
 
6.0%
경상남도 6
 
6.0%
울산광역시 6
 
6.0%
세종특별자치시 6
 
6.0%
충청북도 6
 
6.0%
충청남도 6
 
6.0%
대구광역시 6
 
6.0%
제주특별자치도 6
 
6.0%
서울특별시 6
 
6.0%
Other values (7) 40
40.0%

Length

2023-12-10T20:44:28.264507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 6
 
6.0%
강원도 6
 
6.0%
부산광역시 6
 
6.0%
전라북도 6
 
6.0%
광주광역시 6
 
6.0%
인천광역시 6
 
6.0%
대전광역시 6
 
6.0%
서울특별시 6
 
6.0%
제주특별자치도 6
 
6.0%
대구광역시 6
 
6.0%
Other values (7) 40
40.0%

구분
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
세입
50 
세출
50 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row세입
2nd row세출
3rd row세출
4th row세출
5th row세입

Common Values

ValueCountFrequency (%)
세입 50
50.0%
세출 50
50.0%

Length

2023-12-10T20:44:28.453196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:44:28.585311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세입 50
50.0%
세출 50
50.0%

특별회계 금액(백만원)
Categorical

Distinct39
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
43 
418
 
2
398
 
2
848
 
2
528
 
2
Other values (34)
49 

Length

Max length5
Median length3
Mean length2.14
Min length1

Unique

Unique19 ?
Unique (%)19.0%

Sample

1st row0
2nd row101
3rd row538
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43
43.0%
418 2
 
2.0%
398 2
 
2.0%
848 2
 
2.0%
528 2
 
2.0%
192 2
 
2.0%
33 2
 
2.0%
1,647 2
 
2.0%
124 2
 
2.0%
538 2
 
2.0%
Other values (29) 39
39.0%

Length

2023-12-10T20:44:28.753620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 43
43.0%
442 2
 
2.0%
418 2
 
2.0%
474 2
 
2.0%
542 2
 
2.0%
47 2
 
2.0%
41 2
 
2.0%
61 2
 
2.0%
101 2
 
2.0%
208 2
 
2.0%
Other values (29) 39
39.0%

일반회계 금액(백만원)
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
99 
706
 
1

Length

Max length3
Median length1
Mean length1.02
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 99
99.0%
706 1
 
1.0%

Length

2023-12-10T20:44:28.988909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:44:29.168665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 99
99.0%
706 1
 
1.0%

Correlations

2023-12-10T20:44:29.270542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도행정구역명구분특별회계 금액(백만원)일반회계 금액(백만원)
년도1.0000.0000.0000.8100.023
행정구역명0.0001.0000.0000.9310.000
구분0.0000.0001.0000.0000.000
특별회계 금액(백만원)0.8100.9310.0001.0000.000
일반회계 금액(백만원)0.0230.0000.0000.0001.000
2023-12-10T20:44:29.445134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특별회계 금액(백만원)구분행정구역명년도일반회계 금액(백만원)
특별회계 금액(백만원)1.0000.0000.4900.4400.000
구분0.0001.0000.0000.0000.000
행정구역명0.4900.0001.0000.0000.000
년도0.4400.0000.0001.0000.036
일반회계 금액(백만원)0.0000.0000.0000.0361.000
2023-12-10T20:44:29.598080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도행정구역명구분특별회계 금액(백만원)일반회계 금액(백만원)
년도1.0000.0000.0000.4400.036
행정구역명0.0001.0000.0000.4900.000
구분0.0000.0001.0000.0000.000
특별회계 금액(백만원)0.4400.4900.0001.0000.000
일반회계 금액(백만원)0.0360.0000.0000.0001.000

Missing values

2023-12-10T20:44:27.602543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:44:27.782537image/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

년도행정구역명구분특별회계 금액(백만원)일반회계 금액(백만원)
02008경상북도세입00
12008강원도세출1010
22008울산광역시세출5380
32008경상북도세출00
42008세종특별자치시세입00
52008충청북도세출1240
62008경기도세출8480
72008충청남도세출5280
82008대구광역시세출00
92008제주특별자치도세입00
년도행정구역명구분특별회계 금액(백만원)일반회계 금액(백만원)
902010인천광역시세입2620
912010서울특별시세입1500
922010부산광역시세입1,5650
932010인천광역시세출00
942010대구광역시세입00
952010세종특별자치시세입00
962010제주특별자치도세출00
972010경상남도세출4210
982010경상북도세출00
992010경기도세출4100