Overview

Dataset statistics

Number of variables6
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory53.4 B

Variable types

Numeric1
Categorical3
Text2

Dataset

Description대전광역시 동구의 폐건전지 수거함 설치 현황에 관한 데이터로 연번, 행정동, 주소, 구분, 개수 등의 데이터를 포함하고 있습니다.
Author대전광역시 동구
URLhttps://www.data.go.kr/data/15127062/fileData.do

Alerts

데이터기준일 has constant value ""Constant
연번 is highly overall correlated with 행정동High correlation
행정동 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2024-03-23 06:27:18.675071
Analysis finished2024-03-23 06:27:20.813266
Duration2.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-03-23T06:27:21.032904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.9
Q110.5
median20
Q329.5
95-th percentile37.1
Maximum39
Range38
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.401754
Coefficient of variation (CV)0.57008771
Kurtosis-1.2
Mean20
Median Absolute Deviation (MAD)10
Skewness0
Sum780
Variance130
MonotonicityStrictly increasing
2024-03-23T06:27:21.592288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1 1
 
2.6%
2 1
 
2.6%
23 1
 
2.6%
24 1
 
2.6%
25 1
 
2.6%
26 1
 
2.6%
27 1
 
2.6%
28 1
 
2.6%
29 1
 
2.6%
30 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
1 1
2.6%
2 1
2.6%
3 1
2.6%
4 1
2.6%
5 1
2.6%
6 1
2.6%
7 1
2.6%
8 1
2.6%
9 1
2.6%
10 1
2.6%
ValueCountFrequency (%)
39 1
2.6%
38 1
2.6%
37 1
2.6%
36 1
2.6%
35 1
2.6%
34 1
2.6%
33 1
2.6%
32 1
2.6%
31 1
2.6%
30 1
2.6%

행정동
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Memory size444.0 B
성남동
7 
용운동
6 
용전동
5 
낭월동
4 
판암동
4 
Other values (9)
13 

Length

Max length3
Median length3
Mean length2.974359
Min length2

Unique

Unique7 ?
Unique (%)17.9%

Sample

1st row가오동
2nd row가오동
3rd row가오동
4th row낭월동
5th row낭월동

Common Values

ValueCountFrequency (%)
성남동 7
17.9%
용운동 6
15.4%
용전동 5
12.8%
낭월동 4
10.3%
판암동 4
10.3%
가오동 3
7.7%
삼성동 3
7.7%
대동 1
 
2.6%
대성동 1
 
2.6%
삼정동 1
 
2.6%
Other values (4) 4
10.3%

Length

2024-03-23T06:27:22.128518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성남동 7
17.9%
용운동 6
15.4%
용전동 5
12.8%
낭월동 4
10.3%
판암동 4
10.3%
가오동 3
7.7%
삼성동 3
7.7%
대동 1
 
2.6%
대성동 1
 
2.6%
삼정동 1
 
2.6%
Other values (4) 4
10.3%

주소
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2024-03-23T06:27:22.930374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length25
Mean length23.692308
Min length16

Characters and Unicode

Total characters924
Distinct characters77
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st row대전광역시 동구 은어송로 116(가오동)
2nd row대전광역시 동구 은어송로 100(가오동)
3rd row대전광역시 동구 동구청로 66 (은어송7단지 아파트)
4th row대전광역시 동구 석천로 99-16(낭월동)
5th row대전광역시 동구 산내로 1257번길 40(낭월동)
ValueCountFrequency (%)
대전광역시 39
24.1%
동구 39
24.1%
40(낭월동 2
 
1.2%
은어송로 2
 
1.2%
대전로 2
 
1.2%
용운로 2
 
1.2%
산내로 2
 
1.2%
203(용운동 1
 
0.6%
12(용운동 1
 
0.6%
동부로85번길 1
 
0.6%
Other values (71) 71
43.8%
2024-03-23T06:27:24.794971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
123
 
13.3%
동 83
 
9.0%
전 47
 
5.1%
대 46
 
5.0%
구 41
 
4.4%
시 39
 
4.2%
역 39
 
4.2%
광 39
 
4.2%
) 37
 
4.0%
( 37
 
4.0%
Other values (67) 393
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 557
60.3%
Decimal Number 163
 
17.6%
Space Separator 123
 
13.3%
Close Punctuation 37
 
4.0%
Open Punctuation 37
 
4.0%
Dash Punctuation 7
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 83
14.9%
전 47
 
8.4%
대 46
 
8.3%
구 41
 
7.4%
시 39
 
7.0%
역 39
 
7.0%
광 39
 
7.0%
로 36
 
6.5%
길 21
 
3.8%
번 20
 
3.6%
Other values (53) 146
26.2%
Decimal Number
ValueCountFrequency (%)
1 36
22.1%
3 20
12.3%
2 19
11.7%
0 17
10.4%
5 16
9.8%
4 14
 
8.6%
6 13
 
8.0%
7 12
 
7.4%
9 10
 
6.1%
8 6
 
3.7%
Space Separator
ValueCountFrequency (%)
123
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 557
60.3%
Common 367
39.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 83
14.9%
전 47
 
8.4%
대 46
 
8.3%
구 41
 
7.4%
시 39
 
7.0%
역 39
 
7.0%
광 39
 
7.0%
로 36
 
6.5%
길 21
 
3.8%
번 20
 
3.6%
Other values (53) 146
26.2%
Common
ValueCountFrequency (%)
123
33.5%
) 37
 
10.1%
( 37
 
10.1%
1 36
 
9.8%
3 20
 
5.4%
2 19
 
5.2%
0 17
 
4.6%
5 16
 
4.4%
4 14
 
3.8%
6 13
 
3.5%
Other values (4) 35
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 557
60.3%
ASCII 367
39.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123
33.5%
) 37
 
10.1%
( 37
 
10.1%
1 36
 
9.8%
3 20
 
5.4%
2 19
 
5.2%
0 17
 
4.6%
5 16
 
4.4%
4 14
 
3.8%
6 13
 
3.5%
Other values (4) 35
 
9.5%
Hangul
ValueCountFrequency (%)
동 83
14.9%
전 47
 
8.4%
대 46
 
8.3%
구 41
 
7.4%
시 39
 
7.0%
역 39
 
7.0%
광 39
 
7.0%
로 36
 
6.5%
길 21
 
3.8%
번 20
 
3.6%
Other values (53) 146
26.2%

구분
Text

Distinct21
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Memory size444.0 B
2024-03-23T06:27:25.447739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.6666667
Min length4

Characters and Unicode

Total characters260
Distinct characters78
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

Unique19 ?
Unique (%)48.7%

Sample

1st row은어송로마을4단지아파트
2nd row은어송마을5단지아파트
3rd row은어송7단지아파트
4th row단독주택
5th row석천들주공아파트
ValueCountFrequency (%)
단독주택 18
41.9%
행정복지센터 3
 
7.0%
석천들주공아파트 2
 
4.7%
모리아파트 1
 
2.3%
은어송로마을4단지아파트 1
 
2.3%
용운동 1
 
2.3%
판암주공5단지아파트 1
 
2.3%
판암주공4단지아파트 1
 
2.3%
신한미리내아파트 1
 
2.3%
한울아파트 1
 
2.3%
Other values (13) 13
30.2%
2024-03-23T06:27:26.723021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
단 23
 
8.8%
주 22
 
8.5%
택 18
 
6.9%
독 18
 
6.9%
트 17
 
6.5%
아 17
 
6.5%
파 17
 
6.5%
지 9
 
3.5%
복 4
 
1.5%
정 4
 
1.5%
Other values (68) 111
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 250
96.2%
Decimal Number 5
 
1.9%
Space Separator 4
 
1.5%
Lowercase Letter 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
단 23
 
9.2%
주 22
 
8.8%
택 18
 
7.2%
독 18
 
7.2%
트 17
 
6.8%
아 17
 
6.8%
파 17
 
6.8%
지 9
 
3.6%
복 4
 
1.6%
정 4
 
1.6%
Other values (63) 101
40.4%
Decimal Number
ValueCountFrequency (%)
5 2
40.0%
4 2
40.0%
7 1
20.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 250
96.2%
Common 9
 
3.5%
Latin 1
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
단 23
 
9.2%
주 22
 
8.8%
택 18
 
7.2%
독 18
 
7.2%
트 17
 
6.8%
아 17
 
6.8%
파 17
 
6.8%
지 9
 
3.6%
복 4
 
1.6%
정 4
 
1.6%
Other values (63) 101
40.4%
Common
ValueCountFrequency (%)
4
44.4%
5 2
22.2%
4 2
22.2%
7 1
 
11.1%
Latin
ValueCountFrequency (%)
e 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 250
96.2%
ASCII 10
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
단 23
 
9.2%
주 22
 
8.8%
택 18
 
7.2%
독 18
 
7.2%
트 17
 
6.8%
아 17
 
6.8%
파 17
 
6.8%
지 9
 
3.6%
복 4
 
1.6%
정 4
 
1.6%
Other values (63) 101
40.4%
ASCII
ValueCountFrequency (%)
4
40.0%
5 2
20.0%
4 2
20.0%
e 1
 
10.0%
7 1
 
10.0%

개수
Categorical

Distinct5
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size444.0 B
1
29 
2
4 
4
 
2
5
 
2
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row4

Common Values

ValueCountFrequency (%)
1 29
74.4%
2 4
 
10.3%
4 2
 
5.1%
5 2
 
5.1%
3 2
 
5.1%

Length

2024-03-23T06:27:27.369303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T06:27:27.992388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 29
74.4%
2 4
 
10.3%
4 2
 
5.1%
5 2
 
5.1%
3 2
 
5.1%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
2024-03-08
39 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-08
2nd row2024-03-08
3rd row2024-03-08
4th row2024-03-08
5th row2024-03-08

Common Values

ValueCountFrequency (%)
2024-03-08 39
100.0%

Length

2024-03-23T06:27:28.841810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T06:27:29.407812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-08 39
100.0%

Interactions

2024-03-23T06:27:19.723015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T06:27:29.692341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동주소구분개수
연번1.0000.9091.0000.6320.566
행정동0.9091.0001.0000.8540.442
주소1.0001.0001.0001.0001.000
구분0.6320.8541.0001.0000.974
개수0.5660.4421.0000.9741.000
2024-03-23T06:27:29.954875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동개수
행정동1.0000.191
개수0.1911.000
2024-03-23T06:27:30.215820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동개수
연번1.0000.6660.236
행정동0.6661.0000.191
개수0.2360.1911.000

Missing values

2024-03-23T06:27:20.232501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T06:27:20.680182image/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

연번행정동주소구분개수데이터기준일
01가오동대전광역시 동구 은어송로 116(가오동)은어송로마을4단지아파트22024-03-08
12가오동대전광역시 동구 은어송로 100(가오동)은어송마을5단지아파트12024-03-08
23가오동대전광역시 동구 동구청로 66 (은어송7단지 아파트)은어송7단지아파트12024-03-08
34낭월동대전광역시 동구 석천로 99-16(낭월동)단독주택12024-03-08
45낭월동대전광역시 동구 산내로 1257번길 40(낭월동)석천들주공아파트42024-03-08
56낭월동대전광역시 동구 산내로 1375 (낭월동)낭월오투그란데아파트42024-03-08
67낭월동대전광역시 동구 산내로1257번길 40(낭월동)석천들주공아파트22024-03-08
78대동대전광역시 동구 계족로140번길33(대동)펜타뷰아파트52024-03-08
89대성동대전광역시 동구 대전로 340번길 20(대성동)삼익세라믹아파트12024-03-08
910삼성동대전광역시 동구 현암로 19(삼성동)단독주택12024-03-08
연번행정동주소구분개수데이터기준일
2930용전동대전광역시 동구 송촌남로11번길 147(용전동)단독주택12024-03-08
3031용전동대전광역시 동구 계족로432번길 7(용전동)단독주택12024-03-08
3132용전동대전광역시 동구 한밭대로1254번길 36(용전동)단독주택12024-03-08
3233용전동대전광역시 동구 계족로446번길 19(용전동)단독주택12024-03-08
3334용전동대전광역시 동구 계족로450번길 52(용전동)한울아파트12024-03-08
3435판암동대전광역시 동구 옥천로176번길 104(판암동)단독주택12024-03-08
3536판암동대전광역시 동구 동구청로203번길 35(판암동)신한미리내아파트22024-03-08
3637판암동대전광역시 동구 동부로 56-7 (판암주공4단지)판암주공4단지아파트52024-03-08
3738판암동대전광역시 동구 옥천로180번길 23(판암동)판암주공5단지아파트32024-03-08
3839홍도동대전광역시 동구 동산초교로23번길 15(홍도동)경성맨션아파트12024-03-08