Overview

Dataset statistics

Number of variables4
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory891.0 B
Average record size in memory38.7 B

Variable types

DateTime1
Categorical1
Text1
Numeric1

Dataset

Description인천광역시 미추홀구 쓰레기봉투물류시스템 입고현황에 대한 데이터로 입고일자, 업체명, 품목, 입고수량 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15088153/fileData.do

Reproduction

Analysis started2023-12-12 15:18:28.251840
Analysis finished2023-12-12 15:18:28.688674
Duration0.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

입고일자
Date

Distinct7
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
Minimum2023-01-05 00:00:00
Maximum2023-04-20 00:00:00
2023-12-13T00:18:28.753998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:18:28.871416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)

업체명
Categorical

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
영광산업
11 
에덴복지재단
5 
성광디자인
5 
서구구립장애인재활
2 

Length

Max length9
Median length6
Mean length5.0869565
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영광산업 11
47.8%
에덴복지재단 5
21.7%
성광디자인 5
21.7%
서구구립장애인재활 2
 
8.7%

Length

2023-12-13T00:18:29.043572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:18:29.183690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영광산업 11
47.8%
에덴복지재단 5
21.7%
성광디자인 5
21.7%
서구구립장애인재활 2
 
8.7%

품목
Text

Distinct17
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-13T00:18:29.380586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.3043478
Min length6

Characters and Unicode

Total characters191
Distinct characters24
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)47.8%

Sample

1st row일반용 5L
2nd row일반용 10L
3rd row일반용 20L
4th row재사용 10L
5th row재사용 20L
ValueCountFrequency (%)
일반용 8
15.7%
10l 5
9.8%
스티커 5
9.8%
원권 5
9.8%
음식물 5
9.8%
재사용 4
7.8%
20l 4
7.8%
5l 3
 
5.9%
10000 2
 
3.9%
2l 1
 
2.0%
Other values (9) 9
17.6%
2023-12-13T00:18:29.781885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41
21.5%
0 28
14.7%
L 18
 
9.4%
용 13
 
6.8%
1 9
 
4.7%
일 8
 
4.2%
반 8
 
4.2%
5 6
 
3.1%
원 5
 
2.6%
물 5
 
2.6%
Other values (14) 50
26.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80
41.9%
Decimal Number 52
27.2%
Space Separator 41
21.5%
Uppercase Letter 18
 
9.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 13
16.2%
일 8
10.0%
반 8
10.0%
원 5
 
6.2%
물 5
 
6.2%
식 5
 
6.2%
음 5
 
6.2%
권 5
 
6.2%
티 5
 
6.2%
커 5
 
6.2%
Other values (5) 16
20.0%
Decimal Number
ValueCountFrequency (%)
0 28
53.8%
1 9
 
17.3%
5 6
 
11.5%
2 5
 
9.6%
3 2
 
3.8%
7 1
 
1.9%
6 1
 
1.9%
Space Separator
ValueCountFrequency (%)
41
100.0%
Uppercase Letter
ValueCountFrequency (%)
L 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 93
48.7%
Hangul 80
41.9%
Latin 18
 
9.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 13
16.2%
일 8
10.0%
반 8
10.0%
원 5
 
6.2%
물 5
 
6.2%
식 5
 
6.2%
음 5
 
6.2%
권 5
 
6.2%
티 5
 
6.2%
커 5
 
6.2%
Other values (5) 16
20.0%
Common
ValueCountFrequency (%)
41
44.1%
0 28
30.1%
1 9
 
9.7%
5 6
 
6.5%
2 5
 
5.4%
3 2
 
2.2%
7 1
 
1.1%
6 1
 
1.1%
Latin
ValueCountFrequency (%)
L 18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111
58.1%
Hangul 80
41.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41
36.9%
0 28
25.2%
L 18
16.2%
1 9
 
8.1%
5 6
 
5.4%
2 5
 
4.5%
3 2
 
1.8%
7 1
 
0.9%
6 1
 
0.9%
Hangul
ValueCountFrequency (%)
용 13
16.2%
일 8
10.0%
반 8
10.0%
원 5
 
6.2%
물 5
 
6.2%
식 5
 
6.2%
음 5
 
6.2%
권 5
 
6.2%
티 5
 
6.2%
커 5
 
6.2%
Other values (5) 16
20.0%

입고수량
Real number (ℝ)

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean340969.57
Minimum1000
Maximum1332000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T00:18:29.954173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile14000
Q159500
median167000
Q3442000
95-th percentile1231900
Maximum1332000
Range1331000
Interquartile range (IQR)382500

Descriptive statistics

Standard deviation418552.71
Coefficient of variation (CV)1.2275369
Kurtosis0.91532006
Mean340969.57
Median Absolute Deviation (MAD)133600
Skewness1.4684036
Sum7842300
Variance1.7518637 × 1011
MonotonicityNot monotonic
2023-12-13T00:18:30.107071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
14000 2
 
8.7%
167000 1
 
4.3%
205000 1
 
4.3%
1000 1
 
4.3%
20700 1
 
4.3%
58000 1
 
4.3%
60000 1
 
4.3%
64000 1
 
4.3%
145000 1
 
4.3%
75000 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
1000 1
4.3%
14000 2
8.7%
20700 1
4.3%
58000 1
4.3%
59000 1
4.3%
60000 1
4.3%
64000 1
4.3%
75000 1
4.3%
76000 1
4.3%
145000 1
4.3%
ValueCountFrequency (%)
1332000 1
4.3%
1245000 1
4.3%
1114000 1
4.3%
984000 1
4.3%
500000 1
4.3%
495000 1
4.3%
389000 1
4.3%
300600 1
4.3%
300000 1
4.3%
224000 1
4.3%

Interactions

2023-12-13T00:18:28.392982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:18:30.216745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
입고일자업체명품목입고수량
입고일자1.0001.0000.6130.000
업체명1.0001.0000.6790.384
품목0.6130.6791.0000.000
입고수량0.0000.3840.0001.000
2023-12-13T00:18:30.326669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
입고수량업체명
입고수량1.0000.273
업체명0.2731.000

Missing values

2023-12-13T00:18:28.556319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:18:28.652209image/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

입고일자업체명품목입고수량
02023-01-05영광산업일반용 5L167000
12023-01-05영광산업일반용 10L59000
22023-01-05영광산업일반용 20L76000
32023-01-05영광산업재사용 10L14000
42023-01-05영광산업재사용 20L389000
52023-02-09서구구립장애인재활일반용 50L300600
62023-02-09서구구립장애인재활일반용 75L224000
72023-02-13에덴복지재단일반용 5L300000
82023-02-13에덴복지재단일반용 10L1114000
92023-02-13에덴복지재단일반용 20L984000
입고일자업체명품목입고수량
132023-02-23영광산업음식물 2L1332000
142023-02-23영광산업음식물 3L495000
152023-02-23영광산업음식물 5L75000
162023-02-23영광산업음식물 10L145000
172023-03-08성광디자인스티커 1000 원권64000
182023-03-08성광디자인스티커 3000 원권60000
192023-03-08성광디자인스티커 5000 원권58000
202023-03-08성광디자인스티커 10000 원권14000
212023-04-18영광산업사업계용 60L20700
222023-04-20성광디자인스티커 10000 원권1000