Overview

Dataset statistics

Number of variables4
Number of observations125
Missing cells1
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory35.1 B

Variable types

Text1
Numeric2
Categorical1

Dataset

Description부산광역시 사하구 음식물폐기물다량배출사업장 현황에 대한 데이터로 사업장명, 급식인원, 하루 배출량 등의 항목을 제공합니다.
Author부산광역시 사하구
URLhttps://www.data.go.kr/data/15034247/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
인원(명) is highly overall correlated with 일 발생량(kg)High correlation
일 발생량(kg) is highly overall correlated with 인원(명)High correlation
사업장명 has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:26:25.290675
Analysis finished2024-04-29 22:26:26.972616
Duration1.68 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업장명
Text

UNIQUE 

Distinct125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-04-30T07:26:27.118292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length8.76
Min length3

Characters and Unicode

Total characters1095
Distinct characters239
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)100.0%

Sample

1st row감천문화요양병원
2nd row을숙도초등학교
3rd row삼성여자고등학교
4th row감천중학교
5th row부일외국어고등학교
ValueCountFrequency (%)
의료법인 6
 
3.8%
㈜풀무원푸드앤컬처 4
 
2.5%
㈜동원홈푸드 3
 
1.9%
㈜새손 2
 
1.2%
감천문화요양병원 1
 
0.6%
선안요양병원 1
 
0.6%
동주여자중학교 1
 
0.6%
인주의료재단늘사랑요양병원 1
 
0.6%
허브휴양 1
 
0.6%
한방병원 1
 
0.6%
Other values (139) 139
86.9%
2024-04-30T07:26:27.444575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 55
 
5.0%
학 54
 
4.9%
원 51
 
4.7%
등 38
 
3.5%
35
 
3.2%
병 34
 
3.1%
초 26
 
2.4%
㈜ 22
 
2.0%
양 21
 
1.9%
요 18
 
1.6%
Other values (229) 741
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 985
90.0%
Space Separator 35
 
3.2%
Other Symbol 22
 
2.0%
Open Punctuation 16
 
1.5%
Close Punctuation 16
 
1.5%
Decimal Number 9
 
0.8%
Uppercase Letter 7
 
0.6%
Other Punctuation 4
 
0.4%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 55
 
5.6%
학 54
 
5.5%
원 51
 
5.2%
등 38
 
3.9%
병 34
 
3.5%
초 26
 
2.6%
양 21
 
2.1%
요 18
 
1.8%
동 17
 
1.7%
대 16
 
1.6%
Other values (208) 655
66.5%
Decimal Number
ValueCountFrequency (%)
6 2
22.2%
4 1
11.1%
2 1
11.1%
0 1
11.1%
7 1
11.1%
3 1
11.1%
1 1
11.1%
5 1
11.1%
Uppercase Letter
ValueCountFrequency (%)
S 2
28.6%
F 2
28.6%
C 1
14.3%
N 1
14.3%
I 1
14.3%
Other Punctuation
ValueCountFrequency (%)
& 2
50.0%
. 1
25.0%
: 1
25.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1007
92.0%
Common 81
 
7.4%
Latin 7
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 55
 
5.5%
학 54
 
5.4%
원 51
 
5.1%
등 38
 
3.8%
병 34
 
3.4%
초 26
 
2.6%
㈜ 22
 
2.2%
양 21
 
2.1%
요 18
 
1.8%
동 17
 
1.7%
Other values (209) 671
66.6%
Common
ValueCountFrequency (%)
35
43.2%
( 16
19.8%
) 16
19.8%
6 2
 
2.5%
& 2
 
2.5%
4 1
 
1.2%
2 1
 
1.2%
0 1
 
1.2%
7 1
 
1.2%
3 1
 
1.2%
Other values (5) 5
 
6.2%
Latin
ValueCountFrequency (%)
S 2
28.6%
F 2
28.6%
C 1
14.3%
N 1
14.3%
I 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 985
90.0%
ASCII 88
 
8.0%
None 22
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 55
 
5.6%
학 54
 
5.5%
원 51
 
5.2%
등 38
 
3.9%
병 34
 
3.5%
초 26
 
2.6%
양 21
 
2.1%
요 18
 
1.8%
동 17
 
1.7%
대 16
 
1.6%
Other values (208) 655
66.5%
ASCII
ValueCountFrequency (%)
35
39.8%
( 16
18.2%
) 16
18.2%
6 2
 
2.3%
& 2
 
2.3%
S 2
 
2.3%
F 2
 
2.3%
4 1
 
1.1%
C 1
 
1.1%
N 1
 
1.1%
Other values (10) 10
 
11.4%
None
ValueCountFrequency (%)
㈜ 22
100.0%

인원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean516.04488
Minimum100
Maximum3016.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-30T07:26:27.577016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile106
Q1200
median440
Q3670
95-th percentile1166
Maximum3016.58
Range2916.58
Interquartile range (IQR)470

Descriptive statistics

Standard deviation413.05345
Coefficient of variation (CV)0.80042157
Kurtosis10.535125
Mean516.04488
Median Absolute Deviation (MAD)240
Skewness2.4158326
Sum64505.61
Variance170613.15
MonotonicityNot monotonic
2024-04-30T07:26:27.699644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200.0 7
 
5.6%
100.0 6
 
4.8%
150.0 5
 
4.0%
450.0 5
 
4.0%
250.0 5
 
4.0%
120.0 4
 
3.2%
650.0 4
 
3.2%
400.0 3
 
2.4%
900.0 3
 
2.4%
600.0 3
 
2.4%
Other values (63) 80
64.0%
ValueCountFrequency (%)
100.0 6
4.8%
105.0 1
 
0.8%
110.0 1
 
0.8%
120.0 4
3.2%
130.0 1
 
0.8%
140.0 2
 
1.6%
150.0 5
4.0%
160.0 2
 
1.6%
170.0 2
 
1.6%
190.0 1
 
0.8%
ValueCountFrequency (%)
3016.58 1
0.8%
1900.0 1
0.8%
1500.0 2
1.6%
1253.35 1
0.8%
1174.0 1
0.8%
1170.0 1
0.8%
1150.0 1
0.8%
1090.0 1
0.8%
1055.0 1
0.8%
1050.0 1
0.8%

일 발생량(kg)
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)37.1%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean81.958065
Minimum3
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-30T07:26:27.827400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile16
Q140
median71.5
Q3108.25
95-th percentile198.5
Maximum250
Range247
Interquartile range (IQR)68.25

Descriptive statistics

Standard deviation55.523412
Coefficient of variation (CV)0.67746124
Kurtosis1.0466013
Mean81.958065
Median Absolute Deviation (MAD)32.5
Skewness1.0752119
Sum10162.8
Variance3082.8493
MonotonicityNot monotonic
2024-04-30T07:26:27.961195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
120.0 12
 
9.6%
100.0 10
 
8.0%
50.0 9
 
7.2%
30.0 9
 
7.2%
90.0 9
 
7.2%
70.0 7
 
5.6%
40.0 6
 
4.8%
60.0 5
 
4.0%
20.0 4
 
3.2%
80.0 4
 
3.2%
Other values (36) 49
39.2%
ValueCountFrequency (%)
3.0 1
 
0.8%
4.0 1
 
0.8%
6.5 1
 
0.8%
10.0 2
1.6%
13.0 1
 
0.8%
16.0 2
1.6%
19.0 1
 
0.8%
20.0 4
3.2%
21.0 1
 
0.8%
25.0 2
1.6%
ValueCountFrequency (%)
250.0 3
2.4%
233.0 1
 
0.8%
200.0 3
2.4%
190.0 2
1.6%
180.0 2
1.6%
170.0 1
 
0.8%
160.0 1
 
0.8%
150.0 1
 
0.8%
140.0 2
1.6%
130.0 1
 
0.8%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-04-19
125 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-04-19
2nd row2024-04-19
3rd row2024-04-19
4th row2024-04-19
5th row2024-04-19

Common Values

ValueCountFrequency (%)
2024-04-19 125
100.0%

Length

2024-04-30T07:26:28.091573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:26:28.175005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-04-19 125
100.0%

Interactions

2024-04-30T07:26:26.660988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:26:26.461479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:26:26.746406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:26:26.587956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:26:28.228002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인원(명)일 발생량(kg)
인원(명)1.0000.633
일 발생량(kg)0.6331.000
2024-04-30T07:26:28.303522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인원(명)일 발생량(kg)
인원(명)1.0000.622
일 발생량(kg)0.6221.000

Missing values

2024-04-30T07:26:26.860512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:26:26.936250image/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

사업장명인원(명)일 발생량(kg)데이터기준일자
0감천문화요양병원150.050.02024-04-19
1을숙도초등학교760.0100.02024-04-19
2삼성여자고등학교1174.0233.02024-04-19
3감천중학교340.0100.02024-04-19
4부일외국어고등학교1900.0125.02024-04-19
5조이 효 요양병원540.095.02024-04-19
6의료법인 양경의료재단중앙유병원400.0120.02024-04-19
7옥천초등학교680.0120.02024-04-19
8장평중학교450.0120.02024-04-19
9동산요양병원600.073.02024-04-19
사업장명인원(명)일 발생량(kg)데이터기준일자
115의료법인 연송의료재단 제2신창요양병원600.090.02024-04-19
116㈜풀무원푸드앤컬처 창신INC900.0180.02024-04-19
117㈜풀무원푸드앤컬처 서흥 직원식당170.060.02024-04-19
118㈜새손 강남조선소점450.0200.02024-04-19
119푸디스트 주식회사 부산자생한방병원점110.030.02024-04-19
120㈜가내찬(삼진식품)100.030.02024-04-19
121부산광역시교육청유아교육진흥원280.035.02024-04-19
122부산사하경찰서230.019.02024-04-19
123예스밥상(수 실버원)100.064.02024-04-19
124아카데미 F&S(부산보건대학교)280.0150.02024-04-19