Overview

Dataset statistics

Number of variables4
Number of observations177
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 KiB
Average record size in memory34.7 B

Variable types

Numeric2
Text2

Dataset

Description서울특별시 중랑구 관내의 폐건전지 및 폐형광등 분리수거함의 연번,주소,아파트명,위치,보유수량을 제공합니다. 참고해주시기 바랍니다.
URLhttps://www.data.go.kr/data/15038000/fileData.do

Alerts

연번 has unique valuesUnique
세부위치(건물명 또는 상호) has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:02:46.590082
Analysis finished2023-12-11 23:02:47.197807
Duration0.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct177
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89
Minimum1
Maximum177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T08:02:47.260307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.8
Q145
median89
Q3133
95-th percentile168.2
Maximum177
Range176
Interquartile range (IQR)88

Descriptive statistics

Standard deviation51.239633
Coefficient of variation (CV)0.57572621
Kurtosis-1.2
Mean89
Median Absolute Deviation (MAD)44
Skewness0
Sum15753
Variance2625.5
MonotonicityStrictly increasing
2023-12-12T08:02:47.374082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
134 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
120 1
 
0.6%
121 1
 
0.6%
Other values (167) 167
94.4%
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 (%)
177 1
0.6%
176 1
0.6%
175 1
0.6%
174 1
0.6%
173 1
0.6%
172 1
0.6%
171 1
0.6%
170 1
0.6%
169 1
0.6%
168 1
0.6%

주소
Text

Distinct173
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T08:02:47.758381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length19.706215
Min length16

Characters and Unicode

Total characters3488
Distinct characters63
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

Unique169 ?
Unique (%)95.5%

Sample

1st row서울특별시 서울특별시 중랑구 겸재로61길 40
2nd row서울특별시 서울특별시 중랑구 용마산로 116길 76
3rd row서울특별시 서울특별시 중랑구 망우로67길 10
4th row서울특별시 서울특별시 중랑구 용마산로 115길 109
5th row서울특별시 서울특별시 중랑구 용마산로 115길 109
ValueCountFrequency (%)
서울특별시 183
23.2%
중랑구 177
22.4%
용마산로 28
 
3.5%
동일로 21
 
2.7%
봉화산로 20
 
2.5%
신내로 15
 
1.9%
망우로 11
 
1.4%
공릉로 9
 
1.1%
양원역로 6
 
0.8%
19 6
 
0.8%
Other values (229) 313
39.7%
2023-12-12T08:02:48.220404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
617
17.7%
중 188
 
5.4%
랑 185
 
5.3%
서 184
 
5.3%
시 184
 
5.3%
울 184
 
5.3%
특 183
 
5.2%
별 183
 
5.2%
구 177
 
5.1%
로 170
 
4.9%
Other values (53) 1233
35.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2209
63.3%
Decimal Number 635
 
18.2%
Space Separator 617
 
17.7%
Dash Punctuation 11
 
0.3%
Close Punctuation 8
 
0.2%
Open Punctuation 8
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 188
 
8.5%
랑 185
 
8.4%
서 184
 
8.3%
시 184
 
8.3%
울 184
 
8.3%
특 183
 
8.3%
별 183
 
8.3%
구 177
 
8.0%
로 170
 
7.7%
길 99
 
4.5%
Other values (39) 472
21.4%
Decimal Number
ValueCountFrequency (%)
1 136
21.4%
2 90
14.2%
5 65
10.2%
3 61
9.6%
4 59
9.3%
9 56
8.8%
7 49
 
7.7%
6 47
 
7.4%
0 38
 
6.0%
8 34
 
5.4%
Space Separator
ValueCountFrequency (%)
617
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2209
63.3%
Common 1279
36.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 188
 
8.5%
랑 185
 
8.4%
서 184
 
8.3%
시 184
 
8.3%
울 184
 
8.3%
특 183
 
8.3%
별 183
 
8.3%
구 177
 
8.0%
로 170
 
7.7%
길 99
 
4.5%
Other values (39) 472
21.4%
Common
ValueCountFrequency (%)
617
48.2%
1 136
 
10.6%
2 90
 
7.0%
5 65
 
5.1%
3 61
 
4.8%
4 59
 
4.6%
9 56
 
4.4%
7 49
 
3.8%
6 47
 
3.7%
0 38
 
3.0%
Other values (4) 61
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2209
63.3%
ASCII 1279
36.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
617
48.2%
1 136
 
10.6%
2 90
 
7.0%
5 65
 
5.1%
3 61
 
4.8%
4 59
 
4.6%
9 56
 
4.4%
7 49
 
3.8%
6 47
 
3.7%
0 38
 
3.0%
Other values (4) 61
 
4.8%
Hangul
ValueCountFrequency (%)
중 188
 
8.5%
랑 185
 
8.4%
서 184
 
8.3%
시 184
 
8.3%
울 184
 
8.3%
특 183
 
8.3%
별 183
 
8.3%
구 177
 
8.0%
로 170
 
7.7%
길 99
 
4.5%
Other values (39) 472
21.4%

세부위치(건물명 또는 상호)
Text

UNIQUE 

Distinct177
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T08:02:48.480758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length38
Mean length21.740113
Min length8

Characters and Unicode

Total characters3848
Distinct characters284
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177 ?
Unique (%)100.0%

Sample

1st row망우3동주민센터 옆문 앞
2nd row청광 오뜨빌경비실
3rd row망우본동주민센터 입구 오른쪽 화단
4th row한일 써너스빌 리젠시101동
5th row한일 써너스빌 리젠시 201동 지하 1층
ValueCountFrequency (%)
선별장 34
 
4.1%
신내 32
 
3.8%
입구 25
 
3.0%
옆 21
 
2.5%
면목 20
 
2.4%
묵동 16
 
1.9%
쓰레기 16
 
1.9%
경비실 15
 
1.8%
주차장 15
 
1.8%
건물 14
 
1.7%
Other values (432) 628
75.1%
2023-12-12T08:02:48.874744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
680
 
17.7%
동 238
 
6.2%
1 228
 
5.9%
0 133
 
3.5%
, 127
 
3.3%
2 75
 
1.9%
장 72
 
1.9%
신 63
 
1.6%
) 56
 
1.5%
( 56
 
1.5%
Other values (274) 2120
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2291
59.5%
Space Separator 680
 
17.7%
Decimal Number 600
 
15.6%
Other Punctuation 132
 
3.4%
Close Punctuation 56
 
1.5%
Open Punctuation 56
 
1.5%
Uppercase Letter 22
 
0.6%
Math Symbol 10
 
0.3%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 238
 
10.4%
장 72
 
3.1%
신 63
 
2.7%
내 45
 
2.0%
차 42
 
1.8%
별 38
 
1.7%
주 38
 
1.7%
실 37
 
1.6%
구 37
 
1.6%
지 37
 
1.6%
Other values (241) 1644
71.8%
Uppercase Letter
ValueCountFrequency (%)
L 3
13.6%
S 3
13.6%
B 3
13.6%
H 2
9.1%
A 2
9.1%
G 1
 
4.5%
C 1
 
4.5%
O 1
 
4.5%
X 1
 
4.5%
M 1
 
4.5%
Other values (4) 4
18.2%
Decimal Number
ValueCountFrequency (%)
1 228
38.0%
0 133
22.2%
2 75
 
12.5%
4 40
 
6.7%
3 40
 
6.7%
5 26
 
4.3%
7 17
 
2.8%
6 17
 
2.8%
8 13
 
2.2%
9 11
 
1.8%
Other Punctuation
ValueCountFrequency (%)
, 127
96.2%
/ 3
 
2.3%
' 2
 
1.5%
Math Symbol
ValueCountFrequency (%)
~ 9
90.0%
+ 1
 
10.0%
Space Separator
ValueCountFrequency (%)
680
100.0%
Close Punctuation
ValueCountFrequency (%)
) 56
100.0%
Open Punctuation
ValueCountFrequency (%)
( 56
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2291
59.5%
Common 1534
39.9%
Latin 23
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 238
 
10.4%
장 72
 
3.1%
신 63
 
2.7%
내 45
 
2.0%
차 42
 
1.8%
별 38
 
1.7%
주 38
 
1.7%
실 37
 
1.6%
구 37
 
1.6%
지 37
 
1.6%
Other values (241) 1644
71.8%
Common
ValueCountFrequency (%)
680
44.3%
1 228
 
14.9%
0 133
 
8.7%
, 127
 
8.3%
2 75
 
4.9%
) 56
 
3.7%
( 56
 
3.7%
4 40
 
2.6%
3 40
 
2.6%
5 26
 
1.7%
Other values (8) 73
 
4.8%
Latin
ValueCountFrequency (%)
L 3
13.0%
S 3
13.0%
B 3
13.0%
H 2
 
8.7%
A 2
 
8.7%
G 1
 
4.3%
C 1
 
4.3%
O 1
 
4.3%
X 1
 
4.3%
M 1
 
4.3%
Other values (5) 5
21.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2291
59.5%
ASCII 1557
40.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
680
43.7%
1 228
 
14.6%
0 133
 
8.5%
, 127
 
8.2%
2 75
 
4.8%
) 56
 
3.6%
( 56
 
3.6%
4 40
 
2.6%
3 40
 
2.6%
5 26
 
1.7%
Other values (23) 96
 
6.2%
Hangul
ValueCountFrequency (%)
동 238
 
10.4%
장 72
 
3.1%
신 63
 
2.7%
내 45
 
2.0%
차 42
 
1.8%
별 38
 
1.7%
주 38
 
1.7%
실 37
 
1.6%
구 37
 
1.6%
지 37
 
1.6%
Other values (241) 1644
71.8%

보유 수량
Real number (ℝ)

Distinct10
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8644068
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T08:02:48.971912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum15
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.0123846
Coefficient of variation (CV)1.0793699
Kurtosis15.572948
Mean1.8644068
Median Absolute Deviation (MAD)0
Skewness3.5980043
Sum330
Variance4.0496918
MonotonicityNot monotonic
2023-12-12T08:02:49.063975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 126
71.2%
2 18
 
10.2%
3 13
 
7.3%
4 6
 
3.4%
5 6
 
3.4%
10 3
 
1.7%
6 2
 
1.1%
11 1
 
0.6%
7 1
 
0.6%
15 1
 
0.6%
ValueCountFrequency (%)
1 126
71.2%
2 18
 
10.2%
3 13
 
7.3%
4 6
 
3.4%
5 6
 
3.4%
6 2
 
1.1%
7 1
 
0.6%
10 3
 
1.7%
11 1
 
0.6%
15 1
 
0.6%
ValueCountFrequency (%)
15 1
 
0.6%
11 1
 
0.6%
10 3
 
1.7%
7 1
 
0.6%
6 2
 
1.1%
5 6
 
3.4%
4 6
 
3.4%
3 13
 
7.3%
2 18
 
10.2%
1 126
71.2%

Interactions

2023-12-12T08:02:46.928081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:02:46.779451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:02:47.006307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:02:46.856677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:02:49.158508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번보유 수량
연번1.0000.237
보유 수량0.2371.000
2023-12-12T08:02:49.259447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번보유 수량
연번1.0000.216
보유 수량0.2161.000

Missing values

2023-12-12T08:02:47.100940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:02:47.169335image/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

연번주소세부위치(건물명 또는 상호)보유 수량
01서울특별시 서울특별시 중랑구 겸재로61길 40망우3동주민센터 옆문 앞1
12서울특별시 서울특별시 중랑구 용마산로 116길 76청광 오뜨빌경비실1
23서울특별시 서울특별시 중랑구 망우로67길 10망우본동주민센터 입구 오른쪽 화단1
34서울특별시 서울특별시 중랑구 용마산로 115길 109한일 써너스빌 리젠시101동1
45서울특별시 서울특별시 중랑구 용마산로 115길 109한일 써너스빌 리젠시 201동 지하 1층1
56서울특별시 서울특별시 중랑구 망우로 60길 37상봉 써너스빌 에코오피스텔쓰레기 선별장1
67서울특별시 중랑구 봉우재로 57길 47문수 해오름 아파트지상 2층 주차장 (상봉 이마트 옆)1
78서울특별시 중랑구 상봉로 26길 29가야 써니빌음식물 선별장 (상봉 이마트 옆)1
89서울특별시 중랑구 용마산로 112가길 34예성 아파트101동 음식물 선별장 (망우본동 삼룡사 옆)1
910서울특별시 중랑구 송림길 124개나리 아파트정문 입구 (양원역)1
연번주소세부위치(건물명 또는 상호)보유 수량
167168서울특별시 중랑구 중랑역로 72동구 햇살쓰레기 선별장 (태능시장 오거리)1
168169서울특별시 중랑구 동일로 129길 35태능에셈빌1층 로비 안 지하 계단 앞 (태능시장 오거리)1
169170서울특별시 중랑구 중랑역로 124중화 삼익103동 (우성아파트 아래쪽)1
170171서울특별시 중랑구 봉화산로 301신내역 힐데스하임 참좋은아파트 각동 우편함(501동 / 502동 1~2호, 3~4호 / 503동 1~2호, 3~4호)5
171172서울특별시 중랑구 양원역로 92서울양원S1BL아파트(LH양원1단지) 4개동 재활용 수거장5
172173서울특별시 중랑구 망우동 26신내역금강펜테리움센트럴파크 선별장(5개) 당 2개씩10
173174서울특별시 중랑구 망우동 58-8양원역금호어울림포레스트 선별장 1~4동 / 상가5
174175서울특별시 중랑구 용마산로 209쌍용더플래티넘용마산 선별장2
175176서울특별시 중랑구 봉우재로 32길 58삼성디아이빌 101동 주차장 쪽1
176177서울특별시 중랑구 동일로143길 19제일프라자 상가 주차장(백두산 사우나 건물)1