Overview

Dataset statistics

Number of variables7
Number of observations277
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.5 KiB
Average record size in memory57.5 B

Variable types

Numeric1
Categorical2
Text3
DateTime1

Dataset

Description광주광역시 남구 의류수거함 설치 및 운영관리 조례에 따라 설치한 관내 의류수거함 관련 데이터로, 설치위치, 일련번호, 담당부서, 데이터기준일자를 제공합니다.
Author광주광역시 남구
URLhttps://www.data.go.kr/data/15122304/fileData.do

Alerts

담당부서 has constant value ""Constant
데이터기준일 has constant value ""Constant
연번 is highly overall correlated with 설치동High correlation
설치동 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-23 05:12:25.192779
Analysis finished2024-03-23 05:12:27.192735
Duration2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct277
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139
Minimum1
Maximum277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-23T05:12:27.495004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.8
Q170
median139
Q3208
95-th percentile263.2
Maximum277
Range276
Interquartile range (IQR)138

Descriptive statistics

Standard deviation80.10722
Coefficient of variation (CV)0.57631093
Kurtosis-1.2
Mean139
Median Absolute Deviation (MAD)69
Skewness0
Sum38503
Variance6417.1667
MonotonicityStrictly increasing
2024-03-23T05:12:28.075581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
184 1
 
0.4%
190 1
 
0.4%
189 1
 
0.4%
188 1
 
0.4%
187 1
 
0.4%
186 1
 
0.4%
185 1
 
0.4%
183 1
 
0.4%
175 1
 
0.4%
Other values (267) 267
96.4%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
277 1
0.4%
276 1
0.4%
275 1
0.4%
274 1
0.4%
273 1
0.4%
272 1
0.4%
271 1
0.4%
270 1
0.4%
269 1
0.4%
268 1
0.4%

설치동
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
월산동
78 
백운동
45 
주월동
36 
봉선동
27 
진월동
22 
Other values (7)
69 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
월산동 78
28.2%
백운동 45
16.2%
주월동 36
13.0%
봉선동 27
 
9.7%
진월동 22
 
7.9%
사직동 16
 
5.8%
양림동 15
 
5.4%
송하동 14
 
5.1%
방림동 11
 
4.0%
노대동 7
 
2.5%
Other values (2) 6
 
2.2%

Length

2024-03-23T05:12:28.504912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
월산동 78
28.2%
백운동 45
16.2%
주월동 36
13.0%
봉선동 27
 
9.7%
진월동 22
 
7.9%
사직동 16
 
5.8%
양림동 15
 
5.4%
송하동 14
 
5.1%
방림동 11
 
4.0%
노대동 7
 
2.5%
Other values (2) 6
 
2.2%

설치위치
Text

Distinct274
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-03-23T05:12:29.368458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length29
Mean length20.613718
Min length14

Characters and Unicode

Total characters5710
Distinct characters197
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique271 ?
Unique (%)97.8%

Sample

1st row광주광역시 남구 구성로 20번길23
2nd row광주광역시 남구 월산로 132번길 25
3rd row광주광역시 남구 구성로 20번길 15-1
4th row광주광역시 남구 구성로 38번길 5-1
5th row광주광역시 남구 수원지길 2
ValueCountFrequency (%)
광주광역시 277
22.5%
남구 277
22.5%
진다리로 13
 
1.1%
대남대로 12
 
1.0%
금화로 12
 
1.0%
월산로 10
 
0.8%
6 9
 
0.7%
백은로 9
 
0.7%
백양로 9
 
0.7%
12 9
 
0.7%
Other values (352) 594
48.3%
2024-03-23T05:12:30.979855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
955
16.7%
광 569
 
10.0%
남 302
 
5.3%
1 289
 
5.1%
주 285
 
5.0%
구 284
 
5.0%
역 277
 
4.9%
시 277
 
4.9%
길 243
 
4.3%
로 212
 
3.7%
Other values (187) 2017
35.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3511
61.5%
Decimal Number 1062
 
18.6%
Space Separator 955
 
16.7%
Dash Punctuation 95
 
1.7%
Close Punctuation 41
 
0.7%
Open Punctuation 41
 
0.7%
Uppercase Letter 3
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 569
16.2%
남 302
 
8.6%
주 285
 
8.1%
구 284
 
8.1%
역 277
 
7.9%
시 277
 
7.9%
길 243
 
6.9%
로 212
 
6.0%
번 178
 
5.1%
대 64
 
1.8%
Other values (169) 820
23.4%
Decimal Number
ValueCountFrequency (%)
1 289
27.2%
2 165
15.5%
3 117
11.0%
4 87
 
8.2%
7 82
 
7.7%
6 73
 
6.9%
0 68
 
6.4%
5 67
 
6.3%
8 58
 
5.5%
9 56
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
P 1
33.3%
I 1
33.3%
V 1
33.3%
Space Separator
ValueCountFrequency (%)
955
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 95
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3511
61.5%
Common 2196
38.5%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 569
16.2%
남 302
 
8.6%
주 285
 
8.1%
구 284
 
8.1%
역 277
 
7.9%
시 277
 
7.9%
길 243
 
6.9%
로 212
 
6.0%
번 178
 
5.1%
대 64
 
1.8%
Other values (169) 820
23.4%
Common
ValueCountFrequency (%)
955
43.5%
1 289
 
13.2%
2 165
 
7.5%
3 117
 
5.3%
- 95
 
4.3%
4 87
 
4.0%
7 82
 
3.7%
6 73
 
3.3%
0 68
 
3.1%
5 67
 
3.1%
Other values (5) 198
 
9.0%
Latin
ValueCountFrequency (%)
P 1
33.3%
I 1
33.3%
V 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3511
61.5%
ASCII 2199
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
955
43.4%
1 289
 
13.1%
2 165
 
7.5%
3 117
 
5.3%
- 95
 
4.3%
4 87
 
4.0%
7 82
 
3.7%
6 73
 
3.3%
0 68
 
3.1%
5 67
 
3.0%
Other values (8) 201
 
9.1%
Hangul
ValueCountFrequency (%)
광 569
16.2%
남 302
 
8.6%
주 285
 
8.1%
구 284
 
8.1%
역 277
 
7.9%
시 277
 
7.9%
길 243
 
6.9%
로 212
 
6.0%
번 178
 
5.1%
대 64
 
1.8%
Other values (169) 820
23.4%

위도
Text

Distinct218
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-03-23T05:12:31.815054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length9.8592058
Min length6

Characters and Unicode

Total characters2731
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique214 ?
Unique (%)77.3%

Sample

1st row35.15098641
2nd row35.1502215
3rd row35.15103948
4th row35.15174543
5th row35.15092592
ValueCountFrequency (%)
데이터미집계 57
 
20.6%
35.14292176 2
 
0.7%
35.12739108 2
 
0.7%
35.14625021 2
 
0.7%
35.14696947 1
 
0.4%
35.13670267 1
 
0.4%
35.1453352 1
 
0.4%
35.14050986 1
 
0.4%
35.13947538 1
 
0.4%
35.14308608 1
 
0.4%
Other values (208) 208
75.1%
2024-03-23T05:12:33.323541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 428
15.7%
5 358
13.1%
1 348
12.7%
4 221
8.1%
. 220
8.1%
2 150
 
5.5%
7 139
 
5.1%
0 135
 
4.9%
8 134
 
4.9%
6 134
 
4.9%
Other values (7) 464
17.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2169
79.4%
Other Letter 342
 
12.5%
Other Punctuation 220
 
8.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 428
19.7%
5 358
16.5%
1 348
16.0%
4 221
10.2%
2 150
 
6.9%
7 139
 
6.4%
0 135
 
6.2%
8 134
 
6.2%
6 134
 
6.2%
9 122
 
5.6%
Other Letter
ValueCountFrequency (%)
이 57
16.7%
계 57
16.7%
집 57
16.7%
미 57
16.7%
터 57
16.7%
데 57
16.7%
Other Punctuation
ValueCountFrequency (%)
. 220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2389
87.5%
Hangul 342
 
12.5%

Most frequent character per script

Common
ValueCountFrequency (%)
3 428
17.9%
5 358
15.0%
1 348
14.6%
4 221
9.3%
. 220
9.2%
2 150
 
6.3%
7 139
 
5.8%
0 135
 
5.7%
8 134
 
5.6%
6 134
 
5.6%
Hangul
ValueCountFrequency (%)
이 57
16.7%
계 57
16.7%
집 57
16.7%
미 57
16.7%
터 57
16.7%
데 57
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2389
87.5%
Hangul 342
 
12.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 428
17.9%
5 358
15.0%
1 348
14.6%
4 221
9.3%
. 220
9.2%
2 150
 
6.3%
7 139
 
5.8%
0 135
 
5.7%
8 134
 
5.6%
6 134
 
5.6%
Hangul
ValueCountFrequency (%)
이 57
16.7%
계 57
16.7%
집 57
16.7%
미 57
16.7%
터 57
16.7%
데 57
16.7%

경도
Text

Distinct218
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-03-23T05:12:34.405893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length9.8880866
Min length6

Characters and Unicode

Total characters2739
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique214 ?
Unique (%)77.3%

Sample

1st row126.8985781
2nd row126.8987712
3rd row126.8993031
4th row126.9009567
5th row126.9019596
ValueCountFrequency (%)
데이터미집계 57
 
20.6%
126.8938435 2
 
0.7%
126.894546 2
 
0.7%
126.8909973 2
 
0.7%
126.9066183 1
 
0.4%
126.891649 1
 
0.4%
126.9045133 1
 
0.4%
126.9172807 1
 
0.4%
126.916518 1
 
0.4%
126.9137131 1
 
0.4%
Other values (208) 208
75.1%
2024-03-23T05:12:35.666597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 355
13.0%
6 333
12.2%
2 329
12.0%
9 305
11.1%
8 224
8.2%
. 220
8.0%
0 163
 
6.0%
7 132
 
4.8%
3 130
 
4.7%
4 110
 
4.0%
Other values (7) 438
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2177
79.5%
Other Letter 342
 
12.5%
Other Punctuation 220
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 355
16.3%
6 333
15.3%
2 329
15.1%
9 305
14.0%
8 224
10.3%
0 163
7.5%
7 132
 
6.1%
3 130
 
6.0%
4 110
 
5.1%
5 96
 
4.4%
Other Letter
ValueCountFrequency (%)
이 57
16.7%
계 57
16.7%
집 57
16.7%
미 57
16.7%
터 57
16.7%
데 57
16.7%
Other Punctuation
ValueCountFrequency (%)
. 220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2397
87.5%
Hangul 342
 
12.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 355
14.8%
6 333
13.9%
2 329
13.7%
9 305
12.7%
8 224
9.3%
. 220
9.2%
0 163
6.8%
7 132
 
5.5%
3 130
 
5.4%
4 110
 
4.6%
Hangul
ValueCountFrequency (%)
이 57
16.7%
계 57
16.7%
집 57
16.7%
미 57
16.7%
터 57
16.7%
데 57
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2397
87.5%
Hangul 342
 
12.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 355
14.8%
6 333
13.9%
2 329
13.7%
9 305
12.7%
8 224
9.3%
. 220
9.2%
0 163
6.8%
7 132
 
5.5%
3 130
 
5.4%
4 110
 
4.6%
Hangul
ValueCountFrequency (%)
이 57
16.7%
계 57
16.7%
집 57
16.7%
미 57
16.7%
터 57
16.7%
데 57
16.7%

담당부서
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
자원순환과
277 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자원순환과 277
100.0%

Length

2024-03-23T05:12:36.108152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T05:12:36.428072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자원순환과 277
100.0%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum2024-03-18 00:00:00
Maximum2024-03-18 00:00:00
2024-03-23T05:12:36.850719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:12:37.183997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-23T05:12:25.948712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T05:12:37.426759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치동
연번1.0000.902
설치동0.9021.000
2024-03-23T05:12:37.651250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치동
연번1.0000.677
설치동0.6771.000

Missing values

2024-03-23T05:12:26.513257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T05:12:27.043429image/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

연번설치동설치위치위도경도담당부서데이터기준일
01월산동광주광역시 남구 구성로 20번길2335.15098641126.8985781자원순환과2024-03-18
12월산동광주광역시 남구 월산로 132번길 2535.1502215126.8987712자원순환과2024-03-18
23월산동광주광역시 남구 구성로 20번길 15-135.15103948126.8993031자원순환과2024-03-18
34월산동광주광역시 남구 구성로 38번길 5-135.15174543126.9009567자원순환과2024-03-18
45월산동광주광역시 남구 수원지길 235.15092592126.9019596자원순환과2024-03-18
56월산동광주광역시 남구 구성로 20번 가길 535.15138109126.900173자원순환과2024-03-18
67월산동광주광역시 남구 수원지길 23 1데이터미집계데이터미집계자원순환과2024-03-18
78월산동광주광역시 남구 수원지길 2335.15083228126.8998739자원순환과2024-03-18
89월산동광주광역시 남구 월산로 116번길 3735.14985697126.8999453자원순환과2024-03-18
910월산동광주광역시 남구 월산로 116번길 20-1135.14876903126.9001572자원순환과2024-03-18
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