Overview

Dataset statistics

Number of variables7
Number of observations67
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory61.0 B

Variable types

Numeric3
Categorical2
Text1
DateTime1

Dataset

Description대구광역시 남구 관내 거리 쓰레기통 설치 현황에 대한 데이터로 거리 쓰레기통의 상세 설치위치 등의 항목을 제공합니다. (2023. 9. 5. 기준)
Author대구광역시 남구
URLhttps://www.data.go.kr/data/15086945/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 연번High correlation
경도 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
설치위치 has unique valuesUnique

Reproduction

Analysis started2023-12-16 16:03:55.219539
Analysis finished2023-12-16 16:03:58.815446
Duration3.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34
Minimum1
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-16T16:03:59.089042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.3
Q117.5
median34
Q350.5
95-th percentile63.7
Maximum67
Range66
Interquartile range (IQR)33

Descriptive statistics

Standard deviation19.485037
Coefficient of variation (CV)0.57308932
Kurtosis-1.2
Mean34
Median Absolute Deviation (MAD)17
Skewness0
Sum2278
Variance379.66667
MonotonicityStrictly increasing
2023-12-16T16:03:59.491438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
44 1
 
1.5%
50 1
 
1.5%
49 1
 
1.5%
48 1
 
1.5%
47 1
 
1.5%
46 1
 
1.5%
45 1
 
1.5%
43 1
 
1.5%
2 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
67 1
1.5%
66 1
1.5%
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%
58 1
1.5%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
대구광역시
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구광역시 67
100.0%

Length

2023-12-16T16:03:59.807993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T16:04:00.059002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 67
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
남구
67 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남구 67
100.0%

Length

2023-12-16T16:04:00.593004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T16:04:00.887132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남구 67
100.0%

설치위치
Text

UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size668.0 B
2023-12-16T16:04:01.355331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length11
Mean length7.3731343
Min length5

Characters and Unicode

Total characters494
Distinct characters55
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)100.0%

Sample

1st row명덕로 326
2nd row대봉로 172-2
3rd row대봉로 176 건너
4th row명덕로 282
5th row명덕로 248
ValueCountFrequency (%)
대명로 14
 
10.1%
현충로 7
 
5.1%
중앙대로 6
 
4.3%
명덕로 6
 
4.3%
대봉로 4
 
2.9%
앞산순환로 4
 
2.9%
이천로 4
 
2.9%
양지로 3
 
2.2%
효성로 3
 
2.2%
봉덕로 3
 
2.2%
Other values (78) 84
60.9%
2023-12-16T16:04:02.305388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
72
14.6%
로 65
 
13.2%
2 36
 
7.3%
1 34
 
6.9%
대 25
 
5.1%
4 23
 
4.7%
명 22
 
4.5%
9 16
 
3.2%
5 15
 
3.0%
8 12
 
2.4%
Other values (45) 174
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 236
47.8%
Decimal Number 180
36.4%
Space Separator 72
 
14.6%
Dash Punctuation 6
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 65
27.5%
대 25
 
10.6%
명 22
 
9.3%
덕 10
 
4.2%
성 7
 
3.0%
현 7
 
3.0%
충 7
 
3.0%
중 7
 
3.0%
앙 7
 
3.0%
봉 7
 
3.0%
Other values (33) 72
30.5%
Decimal Number
ValueCountFrequency (%)
2 36
20.0%
1 34
18.9%
4 23
12.8%
9 16
8.9%
5 15
8.3%
8 12
 
6.7%
3 12
 
6.7%
7 12
 
6.7%
0 11
 
6.1%
6 9
 
5.0%
Space Separator
ValueCountFrequency (%)
72
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 258
52.2%
Hangul 236
47.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 65
27.5%
대 25
 
10.6%
명 22
 
9.3%
덕 10
 
4.2%
성 7
 
3.0%
현 7
 
3.0%
충 7
 
3.0%
중 7
 
3.0%
앙 7
 
3.0%
봉 7
 
3.0%
Other values (33) 72
30.5%
Common
ValueCountFrequency (%)
72
27.9%
2 36
14.0%
1 34
13.2%
4 23
 
8.9%
9 16
 
6.2%
5 15
 
5.8%
8 12
 
4.7%
3 12
 
4.7%
7 12
 
4.7%
0 11
 
4.3%
Other values (2) 15
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 258
52.2%
Hangul 236
47.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
72
27.9%
2 36
14.0%
1 34
13.2%
4 23
 
8.9%
9 16
 
6.2%
5 15
 
5.8%
8 12
 
4.7%
3 12
 
4.7%
7 12
 
4.7%
0 11
 
4.3%
Other values (2) 15
 
5.8%
Hangul
ValueCountFrequency (%)
로 65
27.5%
대 25
 
10.6%
명 22
 
9.3%
덕 10
 
4.2%
성 7
 
3.0%
현 7
 
3.0%
충 7
 
3.0%
중 7
 
3.0%
앙 7
 
3.0%
봉 7
 
3.0%
Other values (33) 72
30.5%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.84483
Minimum35.830909
Maximum35.857011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-16T16:04:02.709008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.830909
5-th percentile35.83259
Q135.839161
median35.844621
Q335.851443
95-th percentile35.855826
Maximum35.857011
Range0.02610162
Interquartile range (IQR)0.012281105

Descriptive statistics

Standard deviation0.0075297191
Coefficient of variation (CV)0.0002100643
Kurtosis-1.0953447
Mean35.84483
Median Absolute Deviation (MAD)0.0059622
Skewness-0.066920987
Sum2401.6036
Variance5.669667 × 10-5
MonotonicityNot monotonic
2023-12-16T16:04:03.068041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.85552175 2
 
3.0%
35.85483221 1
 
1.5%
35.84560814 1
 
1.5%
35.84604358 1
 
1.5%
35.8413327 1
 
1.5%
35.84328617 1
 
1.5%
35.84351346 1
 
1.5%
35.84409366 1
 
1.5%
35.84454615 1
 
1.5%
35.84911289 1
 
1.5%
Other values (56) 56
83.6%
ValueCountFrequency (%)
35.83090921 1
1.5%
35.83101535 1
1.5%
35.83204408 1
1.5%
35.83238966 1
1.5%
35.83305629 1
1.5%
35.83312845 1
1.5%
35.83488693 1
1.5%
35.83508873 1
1.5%
35.8352632 1
1.5%
35.83534127 1
1.5%
ValueCountFrequency (%)
35.85701083 1
1.5%
35.85650929 1
1.5%
35.85646107 1
1.5%
35.85595692 1
1.5%
35.85552175 2
3.0%
35.85516306 1
1.5%
35.85483221 1
1.5%
35.85446818 1
1.5%
35.85411395 1
1.5%
35.85406277 1
1.5%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.58563
Minimum128.55676
Maximum128.60576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-16T16:04:03.444104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.55676
5-th percentile128.56528
Q1128.57612
median128.58582
Q3128.59785
95-th percentile128.60396
Maximum128.60576
Range0.049001
Interquartile range (IQR)0.0217303

Descriptive statistics

Standard deviation0.012741431
Coefficient of variation (CV)9.9089076 × 10-5
Kurtosis-0.9461321
Mean128.58563
Median Absolute Deviation (MAD)0.0115233
Skewness-0.19936075
Sum8615.237
Variance0.00016234407
MonotonicityNot monotonic
2023-12-16T16:04:03.842738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.5793143 2
 
3.0%
128.6057592 1
 
1.5%
128.5902016 1
 
1.5%
128.5803965 1
 
1.5%
128.5816241 1
 
1.5%
128.5858228 1
 
1.5%
128.5872636 1
 
1.5%
128.5884191 1
 
1.5%
128.5880461 1
 
1.5%
128.5886556 1
 
1.5%
Other values (56) 56
83.6%
ValueCountFrequency (%)
128.5567582 1
1.5%
128.5601743 1
1.5%
128.5646714 1
1.5%
128.5651267 1
1.5%
128.5656272 1
1.5%
128.5660931 1
1.5%
128.5688068 1
1.5%
128.5688191 1
1.5%
128.5688874 1
1.5%
128.5714545 1
1.5%
ValueCountFrequency (%)
128.6057592 1
1.5%
128.604723 1
1.5%
128.6044898 1
1.5%
128.6044258 1
1.5%
128.6028778 1
1.5%
128.6028415 1
1.5%
128.6021829 1
1.5%
128.6009124 1
1.5%
128.6007359 1
1.5%
128.6006291 1
1.5%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
Minimum2023-09-05 00:00:00
Maximum2023-09-05 00:00:00
2023-12-16T16:04:04.177392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:04:04.618245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-16T16:03:57.486635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:03:55.684870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:03:56.645209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:03:57.741913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:03:56.011308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:03:56.935733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:03:57.963485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:03:56.390911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:03:57.238351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T16:04:04.840135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치위치위도경도
연번1.0001.0000.6860.791
설치위치1.0001.0001.0001.000
위도0.6861.0001.0000.318
경도0.7911.0000.3181.000
2023-12-16T16:04:05.148394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.000-0.525-0.849
위도-0.5251.0000.264
경도-0.8490.2641.000

Missing values

2023-12-16T16:03:58.269008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T16:03:58.632820image/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

연번시도명시군구명설치위치위도경도데이터기준일자
01대구광역시남구명덕로 32635.854832128.6057592023-09-05
12대구광역시남구대봉로 172-235.854063128.6028422023-09-05
23대구광역시남구대봉로 176 건너35.854468128.6028782023-09-05
34대구광역시남구명덕로 28235.855163128.6009122023-09-05
45대구광역시남구명덕로 24835.855957128.5971652023-09-05
56대구광역시남구명덕로 199 건너35.856509128.5893642023-09-05
67대구광역시남구이천로 141-535.85396128.5980912023-09-05
78대구광역시남구이천로14135.853924128.5982352023-09-05
89대구광역시남구희망로 19 옆35.847926128.6007362023-09-05
910대구광역시남구희망로 3435.847504128.6021832023-09-05
연번시도명시군구명설치위치위도경도데이터기준일자
5758대구광역시남구현충로 2735.835341128.5792672023-09-05
5859대구광역시남구앞산순환로 47335.83239128.5764122023-09-05
5960대구광역시남구앞산순환로 44335.831015128.5735462023-09-05
6061대구광역시남구앞산순환로 435 옆 고가다리 밑35.830909128.5731252023-09-05
6162대구광역시남구대명로 16235.838857128.5754182023-09-05
6263대구광역시남구대명로 2535.838659128.5601742023-09-05
6364대구광역시남구대명로 6935.839607128.5656272023-09-05
6465대구광역시남구대명로 13535.839583128.5725562023-09-05
6566대구광역시남구대명로 15935.839453128.5752012023-09-05
6667대구광역시남구월배로 48235.836106128.5567582023-09-05