Overview

Dataset statistics

Number of variables7
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory62.6 B

Variable types

Numeric3
Categorical3
Text1

Dataset

Description강남구도시관리공단에서 운영하는 공영주차장의 전기차 충전시설 설치 현황 데이터입니다. 충전시설의 설치 수량, 설치 위치 등의 정보를 확인할 수 있습니다.
Author강남구도시관리공단
URLhttps://www.data.go.kr/data/15126890/fileData.do

Alerts

설치위치 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
실내_실외 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
연번 is highly overall correlated with 설치수량 and 1 other fieldsHigh correlation
의무설치대수 is highly overall correlated with 설치수량High correlation
설치수량 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 설치수량 and 2 other fieldsHigh correlation
연번 has unique valuesUnique
주차장명 has unique valuesUnique
의무설치대수 has 2 (5.6%) zerosZeros
설치수량 has 6 (16.7%) zerosZeros

Reproduction

Analysis started2024-03-15 00:57:10.768729
Analysis finished2024-03-15 00:57:13.957213
Duration3.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.5
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-15T09:57:14.164666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.75
Q19.75
median18.5
Q327.25
95-th percentile34.25
Maximum36
Range35
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation10.535654
Coefficient of variation (CV)0.5694948
Kurtosis-1.2
Mean18.5
Median Absolute Deviation (MAD)9
Skewness0
Sum666
Variance111
MonotonicityStrictly increasing
2024-03-15T09:57:14.592844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 1
 
2.8%
20 1
 
2.8%
22 1
 
2.8%
23 1
 
2.8%
24 1
 
2.8%
25 1
 
2.8%
26 1
 
2.8%
27 1
 
2.8%
28 1
 
2.8%
29 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
1 1
2.8%
2 1
2.8%
3 1
2.8%
4 1
2.8%
5 1
2.8%
6 1
2.8%
7 1
2.8%
8 1
2.8%
9 1
2.8%
10 1
2.8%
ValueCountFrequency (%)
36 1
2.8%
35 1
2.8%
34 1
2.8%
33 1
2.8%
32 1
2.8%
31 1
2.8%
30 1
2.8%
29 1
2.8%
28 1
2.8%
27 1
2.8%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size416.0 B
노외
25 
노상
8 
부설
3 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
노외 25
69.4%
노상 8
 
22.2%
부설 3
 
8.3%

Length

2024-03-15T09:57:15.033161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T09:57:15.373708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노외 25
69.4%
노상 8
 
22.2%
부설 3
 
8.3%

주차장명
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size416.0 B
2024-03-15T09:57:16.124332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8.5
Mean length5.9444444
Min length3

Characters and Unicode

Total characters214
Distinct characters73
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

Unique36 ?
Unique (%)100.0%

Sample

1st row도곡로21길7
2nd row강남치매안심센터
3rd row개포동공원
4th row대청역
5th row언북초교
ValueCountFrequency (%)
역삼문화공원 2
 
5.3%
도곡로21길7 1
 
2.6%
압구정로29길 1
 
2.6%
논현로22길 1
 
2.6%
탄천2호 1
 
2.6%
밤고개로21길 1
 
2.6%
영동대로85길 1
 
2.6%
논현로28길 1
 
2.6%
헌릉로745 1
 
2.6%
대치유수지 1
 
2.6%
Other values (27) 27
71.1%
2024-03-15T09:57:17.340793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 11
 
5.1%
로 10
 
4.7%
교 8
 
3.7%
초 8
 
3.7%
길 8
 
3.7%
문 7
 
3.3%
대 7
 
3.3%
화 7
 
3.3%
1 7
 
3.3%
구 6
 
2.8%
Other values (63) 135
63.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 177
82.7%
Decimal Number 33
 
15.4%
Space Separator 4
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 10
 
5.6%
교 8
 
4.5%
초 8
 
4.5%
길 8
 
4.5%
문 7
 
4.0%
대 7
 
4.0%
화 7
 
4.0%
구 6
 
3.4%
호 5
 
2.8%
삼 5
 
2.8%
Other values (53) 106
59.9%
Decimal Number
ValueCountFrequency (%)
2 11
33.3%
1 7
21.2%
4 3
 
9.1%
8 3
 
9.1%
5 2
 
6.1%
9 2
 
6.1%
6 2
 
6.1%
7 2
 
6.1%
0 1
 
3.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 177
82.7%
Common 37
 
17.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 10
 
5.6%
교 8
 
4.5%
초 8
 
4.5%
길 8
 
4.5%
문 7
 
4.0%
대 7
 
4.0%
화 7
 
4.0%
구 6
 
3.4%
호 5
 
2.8%
삼 5
 
2.8%
Other values (53) 106
59.9%
Common
ValueCountFrequency (%)
2 11
29.7%
1 7
18.9%
4
 
10.8%
4 3
 
8.1%
8 3
 
8.1%
5 2
 
5.4%
9 2
 
5.4%
6 2
 
5.4%
7 2
 
5.4%
0 1
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 177
82.7%
ASCII 37
 
17.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 11
29.7%
1 7
18.9%
4
 
10.8%
4 3
 
8.1%
8 3
 
8.1%
5 2
 
5.4%
9 2
 
5.4%
6 2
 
5.4%
7 2
 
5.4%
0 1
 
2.7%
Hangul
ValueCountFrequency (%)
로 10
 
5.6%
교 8
 
4.5%
초 8
 
4.5%
길 8
 
4.5%
문 7
 
4.0%
대 7
 
4.0%
화 7
 
4.0%
구 6
 
3.4%
호 5
 
2.8%
삼 5
 
2.8%
Other values (53) 106
59.9%

실내_실외
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size416.0 B
실내
18 
실외
18 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
실내 18
50.0%
실외 18
50.0%

Length

2024-03-15T09:57:17.815142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T09:57:18.149718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
실내 18
50.0%
실외 18
50.0%

의무설치대수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6944444
Minimum0
Maximum7
Zeros2
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-15T09:57:18.451542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q11.75
median2
Q34
95-th percentile5.25
Maximum7
Range7
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.635664
Coefficient of variation (CV)0.60705057
Kurtosis0.081010555
Mean2.6944444
Median Absolute Deviation (MAD)1
Skewness0.56592522
Sum97
Variance2.6753968
MonotonicityNot monotonic
2024-03-15T09:57:18.890209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 10
27.8%
4 8
22.2%
1 7
19.4%
3 5
13.9%
5 2
 
5.6%
0 2
 
5.6%
7 1
 
2.8%
6 1
 
2.8%
ValueCountFrequency (%)
0 2
 
5.6%
1 7
19.4%
2 10
27.8%
3 5
13.9%
4 8
22.2%
5 2
 
5.6%
6 1
 
2.8%
7 1
 
2.8%
ValueCountFrequency (%)
7 1
 
2.8%
6 1
 
2.8%
5 2
 
5.6%
4 8
22.2%
3 5
13.9%
2 10
27.8%
1 7
19.4%
0 2
 
5.6%

설치수량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9444444
Minimum0
Maximum8
Zeros6
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-15T09:57:19.164550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6.5
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.228958
Coefficient of variation (CV)0.75700462
Kurtosis-0.31348755
Mean2.9444444
Median Absolute Deviation (MAD)2
Skewness0.49988678
Sum106
Variance4.968254
MonotonicityNot monotonic
2024-03-15T09:57:19.536415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 6
16.7%
4 6
16.7%
0 6
16.7%
2 5
13.9%
1 5
13.9%
6 3
8.3%
5 3
8.3%
8 2
 
5.6%
ValueCountFrequency (%)
0 6
16.7%
1 5
13.9%
2 5
13.9%
3 6
16.7%
4 6
16.7%
5 3
8.3%
6 3
8.3%
8 2
 
5.6%
ValueCountFrequency (%)
8 2
 
5.6%
6 3
8.3%
5 3
8.3%
4 6
16.7%
3 6
16.7%
2 5
13.9%
1 5
13.9%
0 6
16.7%

설치위치
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size416.0 B
지층
12 
지하1층
7 
주차장문의
6 
지하1층,지하2층
4 
지상1층
3 
Other values (3)
4 

Length

Max length9
Median length5
Mean length4.0555556
Min length2

Unique

Unique2 ?
Unique (%)5.6%

Sample

1st row지하1층
2nd row지상2층
3rd row지하1층
4th row지상1층
5th row지하1층,지하2층

Common Values

ValueCountFrequency (%)
지층 12
33.3%
지하1층 7
19.4%
주차장문의 6
16.7%
지하1층,지하2층 4
 
11.1%
지상1층 3
 
8.3%
지하2층 2
 
5.6%
지상2층 1
 
2.8%
지하3층 1
 
2.8%

Length

2024-03-15T09:57:19.971103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T09:57:20.366492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지층 12
33.3%
지하1층 7
19.4%
주차장문의 6
16.7%
지하1층,지하2층 4
 
11.1%
지상1층 3
 
8.3%
지하2층 2
 
5.6%
지상2층 1
 
2.8%
지하3층 1
 
2.8%

Interactions

2024-03-15T09:57:12.575369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:57:11.190231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:57:11.767921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:57:12.780873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:57:11.349210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:57:11.961552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:57:13.051816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:57:11.523359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:57:12.318358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T09:57:20.578747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분주차장명실내_실외의무설치대수설치수량설치위치
연번1.0000.7041.0000.9400.5900.7360.516
구분0.7041.0001.0000.3310.1260.7360.685
주차장명1.0001.0001.0001.0001.0001.0001.000
실내_실외0.9400.3311.0001.0000.2330.4551.000
의무설치대수0.5900.1261.0000.2331.0000.8680.000
설치수량0.7360.7361.0000.4550.8681.0000.790
설치위치0.5160.6851.0001.0000.0000.7901.000
2024-03-15T09:57:20.765310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분설치위치실내_실외
구분1.0000.5200.522
설치위치0.5201.0000.907
실내_실외0.5220.9071.000
2024-03-15T09:57:20.921604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번의무설치대수설치수량구분실내_실외설치위치
연번1.000-0.483-0.5260.3950.7190.332
의무설치대수-0.4831.0000.8080.0000.1400.000
설치수량-0.5260.8081.0000.5800.3020.367
구분0.3950.0000.5801.0000.5220.520
실내_실외0.7190.1400.3020.5221.0000.907
설치위치0.3320.0000.3670.5200.9071.000

Missing values

2024-03-15T09:57:13.414052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T09:57:13.807709image/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

연번구분주차장명실내_실외의무설치대수설치수량설치위치
01노외도곡로21길7실내22지하1층
12노외강남치매안심센터실내23지상2층
23노외개포동공원실내33지하1층
34노외대청역실내33지상1층
45노외언북초교실내56지하1층,지하2층
56노외논현초교실내46지하1층,지하2층
67노외도곡초교실내45지하1층
78노외신구초교실내44지하1층
89노외포이초교실내44지하1층,지하2층
910노외언주초교실내44지상1층
연번구분주차장명실내_실외의무설치대수설치수량설치위치
2627노상압구정로29길실외34지층
2728노상대치유수지실외12지층
2829부설도곡정보문화도서관실내11지하3층
2930부설강남구청실외41지층
3031노외탄천주차장실외20주차장문의
3132노상영동대로96길실외20주차장문의
3233노상영동대로106길실외10주차장문의
3334노상은마아파트실외10주차장문의
3435부설삼성로별관실외01지층
3536노외개포4문화센터실내01지하1층