Overview

Dataset statistics

Number of variables7
Number of observations297
Missing cells12
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.9 KiB
Average record size in memory58.4 B

Variable types

Numeric2
Categorical3
Text2

Dataset

Description대전광역시 대덕구 관내의 폐형광등 및 폐건전지의 행정동, 수거함 주소, 설치위치, 지역구분, 수량 등의 정보를 개방합니다.
Author대전광역시 대덕구
URLhttps://www.data.go.kr/data/15126988/fileData.do

Alerts

데이터기준일 has constant value ""Constant
연번 is highly overall correlated with 행정동High correlation
행정동 is highly overall correlated with 연번High correlation
주소 has 5 (1.7%) missing valuesMissing
수거함 설치위치 has 6 (2.0%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-14 15:05:20.913127
Analysis finished2024-03-14 15:05:23.076438
Duration2.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct297
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149
Minimum1
Maximum297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-15T00:05:23.205905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.8
Q175
median149
Q3223
95-th percentile282.2
Maximum297
Range296
Interquartile range (IQR)148

Descriptive statistics

Standard deviation85.880731
Coefficient of variation (CV)0.57638075
Kurtosis-1.2
Mean149
Median Absolute Deviation (MAD)74
Skewness0
Sum44253
Variance7375.5
MonotonicityStrictly increasing
2024-03-15T00:05:23.550783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
205 1
 
0.3%
203 1
 
0.3%
202 1
 
0.3%
201 1
 
0.3%
200 1
 
0.3%
199 1
 
0.3%
198 1
 
0.3%
197 1
 
0.3%
196 1
 
0.3%
Other values (287) 287
96.6%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
297 1
0.3%
296 1
0.3%
295 1
0.3%
294 1
0.3%
293 1
0.3%
292 1
0.3%
291 1
0.3%
290 1
0.3%
289 1
0.3%
288 1
0.3%

행정동
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
법2동
38 
덕암동
33 
회덕동
30 
대화동
28 
비래동
28 
Other values (7)
140 

Length

Max length4
Median length3
Mean length3.0909091
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
법2동 38
12.8%
덕암동 33
11.1%
회덕동 30
10.1%
대화동 28
9.4%
비래동 28
9.4%
신탄진동 27
9.1%
오정동 26
8.8%
송촌동 25
8.4%
중리동 22
7.4%
석봉동 17
5.7%
Other values (2) 23
7.7%

Length

2024-03-15T00:05:23.979265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
법2동 38
12.8%
덕암동 33
11.1%
회덕동 30
10.1%
대화동 28
9.4%
비래동 28
9.4%
신탄진동 27
9.1%
오정동 26
8.8%
송촌동 25
8.4%
중리동 22
7.4%
석봉동 17
5.7%
Other values (2) 23
7.7%

주소
Text

MISSING 

Distinct246
Distinct (%)84.2%
Missing5
Missing (%)1.7%
Memory size2.4 KiB
2024-03-15T00:05:25.447763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length8.5890411
Min length1

Characters and Unicode

Total characters2508
Distinct characters60
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique230 ?
Unique (%)78.8%

Sample

1st row오정동 16
2nd row오정동 55-5
3rd row오정동 55-5
4th row오정동 55-5
5th row오정동 55-5
ValueCountFrequency (%)
법2동 37
 
6.4%
비래동 28
 
4.8%
송촌동 25
 
4.3%
오정동 25
 
4.3%
신탄진동 24
 
4.1%
중리동 21
 
3.6%
덕암동 20
 
3.4%
석봉동 16
 
2.8%
192-1 14
 
2.4%
읍내동 11
 
1.9%
Other values (268) 360
62.0%
2024-03-15T00:05:27.317753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
292
 
11.6%
1 271
 
10.8%
동 269
 
10.7%
- 221
 
8.8%
2 214
 
8.5%
4 117
 
4.7%
3 110
 
4.4%
5 97
 
3.9%
9 81
 
3.2%
0 77
 
3.1%
Other values (50) 759
30.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1148
45.8%
Other Letter 846
33.7%
Space Separator 292
 
11.6%
Dash Punctuation 221
 
8.8%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 269
31.8%
법 47
 
5.6%
신 32
 
3.8%
촌 32
 
3.8%
래 28
 
3.3%
비 28
 
3.3%
오 27
 
3.2%
정 26
 
3.1%
송 25
 
3.0%
탄 24
 
2.8%
Other values (37) 308
36.4%
Decimal Number
ValueCountFrequency (%)
1 271
23.6%
2 214
18.6%
4 117
10.2%
3 110
9.6%
5 97
 
8.4%
9 81
 
7.1%
0 77
 
6.7%
8 74
 
6.4%
7 58
 
5.1%
6 49
 
4.3%
Space Separator
ValueCountFrequency (%)
292
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 221
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1662
66.3%
Hangul 846
33.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 269
31.8%
법 47
 
5.6%
신 32
 
3.8%
촌 32
 
3.8%
래 28
 
3.3%
비 28
 
3.3%
오 27
 
3.2%
정 26
 
3.1%
송 25
 
3.0%
탄 24
 
2.8%
Other values (37) 308
36.4%
Common
ValueCountFrequency (%)
292
17.6%
1 271
16.3%
- 221
13.3%
2 214
12.9%
4 117
7.0%
3 110
 
6.6%
5 97
 
5.8%
9 81
 
4.9%
0 77
 
4.6%
8 74
 
4.5%
Other values (3) 108
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1662
66.3%
Hangul 846
33.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
292
17.6%
1 271
16.3%
- 221
13.3%
2 214
12.9%
4 117
7.0%
3 110
 
6.6%
5 97
 
5.8%
9 81
 
4.9%
0 77
 
4.6%
8 74
 
4.5%
Other values (3) 108
 
6.5%
Hangul
ValueCountFrequency (%)
동 269
31.8%
법 47
 
5.6%
신 32
 
3.8%
촌 32
 
3.8%
래 28
 
3.3%
비 28
 
3.3%
오 27
 
3.2%
정 26
 
3.1%
송 25
 
3.0%
탄 24
 
2.8%
Other values (37) 308
36.4%

수거함 설치위치
Text

MISSING 

Distinct277
Distinct (%)95.2%
Missing6
Missing (%)2.0%
Memory size2.4 KiB
2024-03-15T00:05:28.197717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.8522337
Min length2

Characters and Unicode

Total characters1994
Distinct characters266
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)91.4%

Sample

1st row대전대화중학교
2nd row양지마을아파트 101동
3rd row양지마을아파트 102동
4th row양지마을아파트 103동
5th row양지마을아파트 104동
ValueCountFrequency (%)
앞 11
 
3.1%
옆 7
 
2.0%
양지마을아파트 7
 
2.0%
삼호아파트 3
 
0.9%
현대아파트 3
 
0.9%
금성백조2차아파트 3
 
0.9%
내 3
 
0.9%
영진로얄아파트 3
 
0.9%
백송아파트 2
 
0.6%
연립 2
 
0.6%
Other values (293) 307
87.5%
2024-03-15T00:05:29.492926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 118
 
5.9%
트 116
 
5.8%
파 112
 
5.6%
동 75
 
3.8%
1 72
 
3.6%
63
 
3.2%
0 38
 
1.9%
대 36
 
1.8%
교 33
 
1.7%
원 33
 
1.7%
Other values (256) 1298
65.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1754
88.0%
Decimal Number 158
 
7.9%
Space Separator 63
 
3.2%
Uppercase Letter 9
 
0.5%
Open Punctuation 3
 
0.2%
Close Punctuation 3
 
0.2%
Other Punctuation 3
 
0.2%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 118
 
6.7%
트 116
 
6.6%
파 112
 
6.4%
동 75
 
4.3%
대 36
 
2.1%
교 33
 
1.9%
원 33
 
1.9%
주 31
 
1.8%
학 30
 
1.7%
공 26
 
1.5%
Other values (235) 1144
65.2%
Decimal Number
ValueCountFrequency (%)
1 72
45.6%
0 38
24.1%
2 16
 
10.1%
3 7
 
4.4%
5 6
 
3.8%
4 6
 
3.8%
7 4
 
2.5%
6 4
 
2.5%
8 4
 
2.5%
9 1
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
G 3
33.3%
K 2
22.2%
T 2
22.2%
S 1
 
11.1%
A 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
& 2
66.7%
/ 1
33.3%
Space Separator
ValueCountFrequency (%)
63
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1755
88.0%
Common 230
 
11.5%
Latin 9
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 118
 
6.7%
트 116
 
6.6%
파 112
 
6.4%
동 75
 
4.3%
대 36
 
2.1%
교 33
 
1.9%
원 33
 
1.9%
주 31
 
1.8%
학 30
 
1.7%
공 26
 
1.5%
Other values (236) 1145
65.2%
Common
ValueCountFrequency (%)
1 72
31.3%
63
27.4%
0 38
16.5%
2 16
 
7.0%
3 7
 
3.0%
5 6
 
2.6%
4 6
 
2.6%
7 4
 
1.7%
6 4
 
1.7%
8 4
 
1.7%
Other values (5) 10
 
4.3%
Latin
ValueCountFrequency (%)
G 3
33.3%
K 2
22.2%
T 2
22.2%
S 1
 
11.1%
A 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1754
88.0%
ASCII 239
 
12.0%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 118
 
6.7%
트 116
 
6.6%
파 112
 
6.4%
동 75
 
4.3%
대 36
 
2.1%
교 33
 
1.9%
원 33
 
1.9%
주 31
 
1.8%
학 30
 
1.7%
공 26
 
1.5%
Other values (235) 1144
65.2%
ASCII
ValueCountFrequency (%)
1 72
30.1%
63
26.4%
0 38
15.9%
2 16
 
6.7%
3 7
 
2.9%
5 6
 
2.5%
4 6
 
2.5%
7 4
 
1.7%
6 4
 
1.7%
8 4
 
1.7%
Other values (10) 19
 
7.9%
None
ValueCountFrequency (%)
㈜ 1
100.0%

지역구분
Categorical

Distinct16
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
공동주택지역
113 
단독주택지역
74 
공동주택
49 
학교
30 
공공기관
15 
Other values (11)
16 

Length

Max length8
Median length6
Mean length5.0606061
Min length2

Unique

Unique6 ?
Unique (%)2.0%

Sample

1st row학교
2nd row공동주택지역
3rd row공동주택지역
4th row공동주택지역
5th row공동주택지역

Common Values

ValueCountFrequency (%)
공동주택지역 113
38.0%
단독주택지역 74
24.9%
공동주택 49
16.5%
학교 30
 
10.1%
공공기관 15
 
5.1%
<NA> 2
 
0.7%
공원 2
 
0.7%
회관 2
 
0.7%
공동지역 2
 
0.7%
대덕산업단지 2
 
0.7%
Other values (6) 6
 
2.0%

Length

2024-03-15T00:05:29.929731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공동주택지역 113
37.9%
단독주택지역 74
24.8%
공동주택 49
16.4%
학교 30
 
10.1%
공공기관 15
 
5.0%
공원 3
 
1.0%
na 2
 
0.7%
회관 2
 
0.7%
공동지역 2
 
0.7%
대덕산업단지 2
 
0.7%
Other values (6) 6
 
2.0%

보유 수량
Real number (ℝ)

Distinct7
Distinct (%)2.4%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.2263514
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-15T00:05:30.265261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.89031409
Coefficient of variation (CV)0.72598615
Kurtosis28.662587
Mean1.2263514
Median Absolute Deviation (MAD)0
Skewness4.936569
Sum363
Variance0.79265918
MonotonicityNot monotonic
2024-03-15T00:05:30.623956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 271
91.2%
2 8
 
2.7%
4 7
 
2.4%
3 4
 
1.3%
5 3
 
1.0%
6 2
 
0.7%
9 1
 
0.3%
(Missing) 1
 
0.3%
ValueCountFrequency (%)
1 271
91.2%
2 8
 
2.7%
3 4
 
1.3%
4 7
 
2.4%
5 3
 
1.0%
6 2
 
0.7%
9 1
 
0.3%
ValueCountFrequency (%)
9 1
 
0.3%
6 2
 
0.7%
5 3
 
1.0%
4 7
 
2.4%
3 4
 
1.3%
2 8
 
2.7%
1 271
91.2%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-03-06
297 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-06
2nd row2024-03-06
3rd row2024-03-06
4th row2024-03-06
5th row2024-03-06

Common Values

ValueCountFrequency (%)
2024-03-06 297
100.0%

Length

2024-03-15T00:05:31.024369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T00:05:31.322511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-06 297
100.0%

Interactions

2024-03-15T00:05:21.868545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:05:21.350931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:05:22.130022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:05:21.603091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T00:05:31.498726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동지역구분보유 수량
연번1.0000.9580.4010.245
행정동0.9581.0000.4940.344
지역구분0.4010.4941.0000.000
보유 수량0.2450.3440.0001.000
2024-03-15T00:05:31.747638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동지역구분
행정동1.0000.203
지역구분0.2031.000
2024-03-15T00:05:31.988826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번보유 수량행정동지역구분
연번1.0000.0530.8320.159
보유 수량0.0531.0000.1730.000
행정동0.8320.1731.0000.203
지역구분0.1590.0000.2031.000

Missing values

2024-03-15T00:05:22.501760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T00:05:22.797513image/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.
2024-03-15T00:05:22.976805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번행정동주소수거함 설치위치지역구분보유 수량데이터기준일
01오정동오정동 16대전대화중학교학교12024-03-06
12오정동오정동 55-5양지마을아파트 101동공동주택지역12024-03-06
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