Overview

Dataset statistics

Number of variables3
Number of observations155
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory25.9 B

Variable types

Numeric1
Categorical1
Text1

Dataset

Description2020년5월 기준의 음식물쓰레기 RFID 지자체 도입 현황에 대한 목록입니다.
Author한국환경공단
URLhttps://www.data.go.kr/data/15061314/fileData.do

Alerts

연번 is highly overall correlated with 광역High correlation
광역 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:48:45.664654
Analysis finished2023-12-12 23:48:46.034542
Duration0.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct155
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78
Minimum1
Maximum155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T08:48:46.101999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.7
Q139.5
median78
Q3116.5
95-th percentile147.3
Maximum155
Range154
Interquartile range (IQR)77

Descriptive statistics

Standard deviation44.888751
Coefficient of variation (CV)0.57549681
Kurtosis-1.2
Mean78
Median Absolute Deviation (MAD)39
Skewness0
Sum12090
Variance2015
MonotonicityStrictly increasing
2023-12-13T08:48:46.227587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
108 1
 
0.6%
101 1
 
0.6%
102 1
 
0.6%
103 1
 
0.6%
104 1
 
0.6%
105 1
 
0.6%
106 1
 
0.6%
107 1
 
0.6%
109 1
 
0.6%
Other values (145) 145
93.5%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
155 1
0.6%
154 1
0.6%
153 1
0.6%
152 1
0.6%
151 1
0.6%
150 1
0.6%
149 1
0.6%
148 1
0.6%
147 1
0.6%
146 1
0.6%

광역
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
경기도
26 
서울특별시
25 
부산광역시
16 
강원도
13 
경상북도
12 
Other values (11)
63 

Length

Max length7
Median length5
Mean length4.2580645
Min length3

Unique

Unique2 ?
Unique (%)1.3%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 26
16.8%
서울특별시 25
16.1%
부산광역시 16
10.3%
강원도 13
8.4%
경상북도 12
7.7%
경상남도 10
 
6.5%
인천광역시 9
 
5.8%
전라남도 9
 
5.8%
대구광역시 8
 
5.2%
전라북도 8
 
5.2%
Other values (6) 19
12.3%

Length

2023-12-13T08:48:46.358629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 26
16.8%
서울특별시 25
16.1%
부산광역시 16
10.3%
강원도 13
8.4%
경상북도 12
7.7%
경상남도 10
 
6.5%
인천광역시 9
 
5.8%
전라남도 9
 
5.8%
대구광역시 8
 
5.2%
전라북도 8
 
5.2%
Other values (6) 19
12.3%

기초
Text

Distinct133
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T08:48:46.694102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.9419355
Min length2

Characters and Unicode

Total characters456
Distinct characters108
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)81.3%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
중구 6
 
3.9%
동구 6
 
3.9%
서구 5
 
3.2%
북구 4
 
2.6%
남구 4
 
2.6%
고성군 2
 
1.3%
강서구 2
 
1.3%
철원군 1
 
0.6%
양구군 1
 
0.6%
고창군 1
 
0.6%
Other values (123) 123
79.4%
2023-12-13T08:48:47.146946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 75
 
16.4%
시 59
 
12.9%
군 30
 
6.6%
동 16
 
3.5%
천 13
 
2.9%
주 13
 
2.9%
서 12
 
2.6%
성 11
 
2.4%
남 10
 
2.2%
산 9
 
2.0%
Other values (98) 208
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 456
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 75
 
16.4%
시 59
 
12.9%
군 30
 
6.6%
동 16
 
3.5%
천 13
 
2.9%
주 13
 
2.9%
서 12
 
2.6%
성 11
 
2.4%
남 10
 
2.2%
산 9
 
2.0%
Other values (98) 208
45.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 456
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 75
 
16.4%
시 59
 
12.9%
군 30
 
6.6%
동 16
 
3.5%
천 13
 
2.9%
주 13
 
2.9%
서 12
 
2.6%
성 11
 
2.4%
남 10
 
2.2%
산 9
 
2.0%
Other values (98) 208
45.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 456
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 75
 
16.4%
시 59
 
12.9%
군 30
 
6.6%
동 16
 
3.5%
천 13
 
2.9%
주 13
 
2.9%
서 12
 
2.6%
성 11
 
2.4%
남 10
 
2.2%
산 9
 
2.0%
Other values (98) 208
45.6%

Interactions

2023-12-13T08:48:45.791088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:48:47.249383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번광역
연번1.0000.944
광역0.9441.000
2023-12-13T08:48:47.333753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번광역
연번1.0000.746
광역0.7461.000

Missing values

2023-12-13T08:48:45.920231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:48:45.999499image/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

연번광역기초
01서울특별시종로구
12서울특별시중구
23서울특별시용산구
34서울특별시성동구
45서울특별시광진구
56서울특별시동대문구
67서울특별시중랑구
78서울특별시성북구
89서울특별시강북구
910서울특별시도봉구
연번광역기초
145146경상남도통영시
146147경상남도김해시
147148경상남도거제시
148149경상남도양산시
149150경상남도의령군
150151경상남도창녕군
151152경상남도고성군
152153경상남도하동군
153154제주특별자치도제주시
154155제주특별자치도서귀포시