Overview

Dataset statistics

Number of variables5
Number of observations45
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory45.9 B

Variable types

Categorical1
Text1
Numeric3

Dataset

Description대전광역시 2021년 공원관리사업소 공원 건물 및 화장실 전등수량입니다. 2022년 공공데이터 기업매칭지원사업으로 수행되었습니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15111055/fileData.do

Alerts

삼파장 is highly overall correlated with 구역High correlation
전구(LED) is highly overall correlated with 구역High correlation
구역 is highly overall correlated with 삼파장 and 1 other fieldsHigh correlation
삼파장 has 26 (57.8%) zerosZeros
전구(LED) has 9 (20.0%) zerosZeros
형광등 has 32 (71.1%) zerosZeros

Reproduction

Analysis started2023-12-12 18:38:31.098634
Analysis finished2023-12-12 18:38:33.319434
Duration2.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구역
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size492.0 B
보문산
26 
장동산림욕장
7 
세천공원
6 
가양공원
4 
만인산
 
1

Length

Max length6
Median length3
Mean length3.6888889
Min length3

Unique

Unique2 ?
Unique (%)4.4%

Sample

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

Common Values

ValueCountFrequency (%)
보문산 26
57.8%
장동산림욕장 7
 
15.6%
세천공원 6
 
13.3%
가양공원 4
 
8.9%
만인산 1
 
2.2%
장태산 1
 
2.2%

Length

2023-12-13T03:38:33.471684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:38:33.718070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보문산 26
57.8%
장동산림욕장 7
 
15.6%
세천공원 6
 
13.3%
가양공원 4
 
8.9%
만인산 1
 
2.2%
장태산 1
 
2.2%

전등 종류
Text

Distinct40
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Memory size492.0 B
2023-12-13T03:38:34.106393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length6.0444444
Min length2

Characters and Unicode

Total characters272
Distinct characters66
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

Unique37 ?
Unique (%)82.2%

Sample

1st row사 무 실
2nd row배 구 장
3rd row축 구 장
4th row레 포 츠
5th row청년광장
ValueCountFrequency (%)
화장실 8
 
9.8%
숲치유센타 5
 
6.1%
사무실 5
 
6.1%
사무실화장실 4
 
4.9%
사 3
 
3.7%
망 3
 
3.7%
물놀이장 2
 
2.4%
대 2
 
2.4%
사무실밑 2
 
2.4%
1층 2
 
2.4%
Other values (39) 46
56.1%
2023-12-13T03:38:34.800928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37
 
13.6%
장 29
 
10.7%
실 24
 
8.8%
사 15
 
5.5%
화 14
 
5.1%
무 12
 
4.4%
숲 9
 
3.3%
치 7
 
2.6%
유 6
 
2.2%
공 6
 
2.2%
Other values (56) 113
41.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 228
83.8%
Space Separator 37
 
13.6%
Decimal Number 7
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 29
 
12.7%
실 24
 
10.5%
사 15
 
6.6%
화 14
 
6.1%
무 12
 
5.3%
숲 9
 
3.9%
치 7
 
3.1%
유 6
 
2.6%
공 6
 
2.6%
층 5
 
2.2%
Other values (53) 101
44.3%
Decimal Number
ValueCountFrequency (%)
1 4
57.1%
2 3
42.9%
Space Separator
ValueCountFrequency (%)
37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 228
83.8%
Common 44
 
16.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 29
 
12.7%
실 24
 
10.5%
사 15
 
6.6%
화 14
 
6.1%
무 12
 
5.3%
숲 9
 
3.9%
치 7
 
3.1%
유 6
 
2.6%
공 6
 
2.6%
층 5
 
2.2%
Other values (53) 101
44.3%
Common
ValueCountFrequency (%)
37
84.1%
1 4
 
9.1%
2 3
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 228
83.8%
ASCII 44
 
16.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37
84.1%
1 4
 
9.1%
2 3
 
6.8%
Hangul
ValueCountFrequency (%)
장 29
 
12.7%
실 24
 
10.5%
사 15
 
6.6%
화 14
 
6.1%
무 12
 
5.3%
숲 9
 
3.9%
치 7
 
3.1%
유 6
 
2.6%
공 6
 
2.6%
층 5
 
2.2%
Other values (53) 101
44.3%

삼파장
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.533333
Minimum0
Maximum224
Zeros26
Zeros (%)57.8%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T03:38:35.013680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile39
Maximum224
Range224
Interquartile range (IQR)12

Descriptive statistics

Standard deviation34.321078
Coefficient of variation (CV)2.975816
Kurtosis35.092217
Mean11.533333
Median Absolute Deviation (MAD)0
Skewness5.6612517
Sum519
Variance1177.9364
MonotonicityNot monotonic
2023-12-13T03:38:35.242551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 26
57.8%
20 2
 
4.4%
8 2
 
4.4%
22 2
 
4.4%
4 1
 
2.2%
224 1
 
2.2%
16 1
 
2.2%
2 1
 
2.2%
3 1
 
2.2%
21 1
 
2.2%
Other values (7) 7
 
15.6%
ValueCountFrequency (%)
0 26
57.8%
1 1
 
2.2%
2 1
 
2.2%
3 1
 
2.2%
4 1
 
2.2%
6 1
 
2.2%
8 2
 
4.4%
12 1
 
2.2%
13 1
 
2.2%
16 1
 
2.2%
ValueCountFrequency (%)
224 1
2.2%
45 1
2.2%
41 1
2.2%
31 1
2.2%
22 2
4.4%
21 1
2.2%
20 2
4.4%
16 1
2.2%
13 1
2.2%
12 1
2.2%

전구(LED)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.822222
Minimum0
Maximum511
Zeros9
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T03:38:35.489331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median8
Q318
95-th percentile179.2
Maximum511
Range511
Interquartile range (IQR)14

Descriptive statistics

Standard deviation88.454993
Coefficient of variation (CV)2.5401881
Kurtosis20.06944
Mean34.822222
Median Absolute Deviation (MAD)6
Skewness4.2462217
Sum1567
Variance7824.2859
MonotonicityNot monotonic
2023-12-13T03:38:35.812306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 9
20.0%
4 5
 
11.1%
6 5
 
11.1%
8 4
 
8.9%
12 2
 
4.4%
18 2
 
4.4%
10 2
 
4.4%
152 1
 
2.2%
2 1
 
2.2%
186 1
 
2.2%
Other values (13) 13
28.9%
ValueCountFrequency (%)
0 9
20.0%
2 1
 
2.2%
4 5
11.1%
5 1
 
2.2%
6 5
11.1%
7 1
 
2.2%
8 4
8.9%
9 1
 
2.2%
10 2
 
4.4%
12 2
 
4.4%
ValueCountFrequency (%)
511 1
2.2%
260 1
2.2%
186 1
2.2%
152 1
2.2%
50 1
2.2%
49 1
2.2%
47 1
2.2%
34 1
2.2%
30 1
2.2%
28 1
2.2%

형광등
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7333333
Minimum0
Maximum47
Zeros32
Zeros (%)71.1%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T03:38:36.066251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile19
Maximum47
Range47
Interquartile range (IQR)2

Descriptive statistics

Standard deviation9.7663616
Coefficient of variation (CV)2.6159897
Kurtosis12.083487
Mean3.7333333
Median Absolute Deviation (MAD)0
Skewness3.4279981
Sum168
Variance95.381818
MonotonicityNot monotonic
2023-12-13T03:38:36.299810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 32
71.1%
2 3
 
6.7%
6 2
 
4.4%
15 2
 
4.4%
1 1
 
2.2%
4 1
 
2.2%
47 1
 
2.2%
40 1
 
2.2%
20 1
 
2.2%
8 1
 
2.2%
ValueCountFrequency (%)
0 32
71.1%
1 1
 
2.2%
2 3
 
6.7%
4 1
 
2.2%
6 2
 
4.4%
8 1
 
2.2%
15 2
 
4.4%
20 1
 
2.2%
40 1
 
2.2%
47 1
 
2.2%
ValueCountFrequency (%)
47 1
 
2.2%
40 1
 
2.2%
20 1
 
2.2%
15 2
 
4.4%
8 1
 
2.2%
6 2
 
4.4%
4 1
 
2.2%
2 3
 
6.7%
1 1
 
2.2%
0 32
71.1%

Interactions

2023-12-13T03:38:32.505445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:38:31.400562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:38:31.953308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:38:32.663255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:38:31.580967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:38:32.118305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:38:32.833758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:38:31.782630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:38:32.309160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:38:36.480883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구역전등 종류삼파장전구(LED)형광등
구역1.0000.0000.6880.7850.000
전등 종류0.0001.0000.0000.0001.000
삼파장0.6880.0001.0000.5930.000
전구(LED)0.7850.0000.5931.0000.000
형광등0.0001.0000.0000.0001.000
2023-12-13T03:38:36.689666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
삼파장전구(LED)형광등구역
삼파장1.000-0.1270.0390.504
전구(LED)-0.1271.000-0.0610.659
형광등0.039-0.0611.0000.000
구역0.5040.6590.0001.000

Missing values

2023-12-13T03:38:33.036357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:38:33.231050image/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

구역전등 종류삼파장전구(LED)형광등
0보문산사 무 실0180
1보문산배 구 장1261
2보문산축 구 장064
3보문산레 포 츠2070
4보문산청년광장31102
5보문산고 촉 사060
6보문산망 향 탑800
7보문산송 학 사802
8보문산목재문화체험장 1층02600
9보문산목재문화체험장 2층01520
구역전등 종류삼파장전구(LED)형광등
35가양공원사무실밑 화장실2242
36가양공원광장 화장실21280
37세천공원세천공원 사무실3015
38세천공원사무실화장실200
39세천공원사무실밑 화장실16120
40세천공원1주차장 화장실040
41세천공원2주차장 화장실048
42세천공원식장산전망대0100
43만인산전체05110
44장태산전체2241860