Overview

Dataset statistics

Number of variables6
Number of observations172
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.7 KiB
Average record size in memory51.7 B

Variable types

Numeric3
Categorical2
Text1

Dataset

Description대구광역시 동구 기계설비 성능점검 대상 건축물 현황 자료입니다. 법정동, 본번, 부번, 건물명을 포함하고 있습니다.
Author대구광역시 동구
URLhttps://www.data.go.kr/data/15112135/fileData.do

Alerts

시군구 has constant value ""Constant
본번 is highly overall correlated with 법정동High correlation
법정동 is highly overall correlated with 본번High correlation
순번 has unique valuesUnique
건물명 has unique valuesUnique
부번 has 109 (63.4%) zerosZeros

Reproduction

Analysis started2024-03-14 13:22:10.523850
Analysis finished2024-03-14 13:22:13.785984
Duration3.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct172
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.5
Minimum1
Maximum172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-03-14T22:22:13.992826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.55
Q143.75
median86.5
Q3129.25
95-th percentile163.45
Maximum172
Range171
Interquartile range (IQR)85.5

Descriptive statistics

Standard deviation49.796252
Coefficient of variation (CV)0.57567921
Kurtosis-1.2
Mean86.5
Median Absolute Deviation (MAD)43
Skewness0
Sum14878
Variance2479.6667
MonotonicityStrictly increasing
2024-03-14T22:22:14.436846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
120 1
 
0.6%
112 1
 
0.6%
113 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%
Other values (162) 162
94.2%
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 (%)
172 1
0.6%
171 1
0.6%
170 1
0.6%
169 1
0.6%
168 1
0.6%
167 1
0.6%
166 1
0.6%
165 1
0.6%
164 1
0.6%
163 1
0.6%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
대구광역시 동구
172 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구광역시 동구 172
100.0%

Length

2024-03-14T22:22:14.871550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:22:15.194792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 172
50.0%
동구 172
50.0%

법정동
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
신서동
30 
신천동
27 
신암동
21 
봉무동
17 
각산동
14 
Other values (17)
63 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique3 ?
Unique (%)1.7%

Sample

1st row신천동
2nd row율하동
3rd row신서동
4th row신암동
5th row봉무동

Common Values

ValueCountFrequency (%)
신서동 30
17.4%
신천동 27
15.7%
신암동 21
12.2%
봉무동 17
9.9%
각산동 14
8.1%
율하동 12
 
7.0%
지묘동 8
 
4.7%
효목동 6
 
3.5%
용계동 5
 
2.9%
방촌동 5
 
2.9%
Other values (12) 27
15.7%

Length

2024-03-14T22:22:15.536539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신서동 30
17.4%
신천동 27
15.7%
신암동 21
12.2%
봉무동 17
9.9%
각산동 14
8.1%
율하동 12
 
7.0%
지묘동 8
 
4.7%
효목동 6
 
3.5%
용계동 5
 
2.9%
방촌동 5
 
2.9%
Other values (12) 27
15.7%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct158
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean860.59302
Minimum36
Maximum1846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-03-14T22:22:15.906663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile108.1
Q1479.5
median844.5
Q31181.75
95-th percentile1560.45
Maximum1846
Range1810
Interquartile range (IQR)702.25

Descriptive statistics

Standard deviation487.69215
Coefficient of variation (CV)0.56669313
Kurtosis-1.0949452
Mean860.59302
Median Absolute Deviation (MAD)357
Skewness0.14648373
Sum148022
Variance237843.63
MonotonicityNot monotonic
2024-03-14T22:22:16.352178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1152 2
 
1.2%
326 2
 
1.2%
1553 2
 
1.2%
327 2
 
1.2%
286 2
 
1.2%
1084 2
 
1.2%
1840 2
 
1.2%
622 2
 
1.2%
483 2
 
1.2%
1537 2
 
1.2%
Other values (148) 152
88.4%
ValueCountFrequency (%)
36 1
0.6%
38 1
0.6%
55 1
0.6%
67 1
0.6%
81 1
0.6%
89 1
0.6%
95 1
0.6%
106 1
0.6%
107 1
0.6%
109 1
0.6%
ValueCountFrequency (%)
1846 1
0.6%
1840 2
1.2%
1839 1
0.6%
1833 1
0.6%
1623 1
0.6%
1621 1
0.6%
1571 1
0.6%
1561 1
0.6%
1560 1
0.6%
1553 2
1.2%

부번
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4534884
Minimum0
Maximum71
Zeros109
Zeros (%)63.4%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-03-14T22:22:16.730944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile12.9
Maximum71
Range71
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.4506673
Coefficient of variation (CV)3.0367649
Kurtosis44.52562
Mean2.4534884
Median Absolute Deviation (MAD)0
Skewness5.8558809
Sum422
Variance55.512444
MonotonicityNot monotonic
2024-03-14T22:22:17.114606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 109
63.4%
1 26
 
15.1%
2 6
 
3.5%
3 5
 
2.9%
4 4
 
2.3%
5 3
 
1.7%
6 3
 
1.7%
9 2
 
1.2%
7 2
 
1.2%
20 2
 
1.2%
Other values (10) 10
 
5.8%
ValueCountFrequency (%)
0 109
63.4%
1 26
 
15.1%
2 6
 
3.5%
3 5
 
2.9%
4 4
 
2.3%
5 3
 
1.7%
6 3
 
1.7%
7 2
 
1.2%
8 1
 
0.6%
9 2
 
1.2%
ValueCountFrequency (%)
71 1
0.6%
31 1
0.6%
30 1
0.6%
27 1
0.6%
23 1
0.6%
21 1
0.6%
20 2
1.2%
14 1
0.6%
12 1
0.6%
11 1
0.6%

건물명
Text

UNIQUE 

Distinct172
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-03-14T22:22:17.940930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length9.4825581
Min length4

Characters and Unicode

Total characters1631
Distinct characters278
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

Unique172 ?
Unique (%)100.0%

Sample

1st row신세계동대구복합환승센터
2nd row롯데쇼핑프라자
3rd row하우스디 어반 메가시티
4th row 대구 파티마병원
5th row롯데아울렛 이시아폴리스점
ValueCountFrequency (%)
이시아폴리스 6
 
2.4%
동대구 5
 
2.0%
더샵 4
 
1.6%
대구혁신도시 3
 
1.2%
동대구역 3
 
1.2%
주식회사 3
 
1.2%
대구 3
 
1.2%
율하 3
 
1.2%
아파트 3
 
1.2%
서한 3
 
1.2%
Other values (203) 214
85.6%
2024-03-14T22:22:19.198581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
83
 
5.1%
구 71
 
4.4%
대 70
 
4.3%
아 45
 
2.8%
스 43
 
2.6%
이 38
 
2.3%
신 38
 
2.3%
동 37
 
2.3%
지 31
 
1.9%
시 29
 
1.8%
Other values (268) 1146
70.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1471
90.2%
Space Separator 83
 
5.1%
Decimal Number 45
 
2.8%
Uppercase Letter 13
 
0.8%
Lowercase Letter 10
 
0.6%
Open Punctuation 4
 
0.2%
Close Punctuation 4
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 71
 
4.8%
대 70
 
4.8%
아 45
 
3.1%
스 43
 
2.9%
이 38
 
2.6%
신 38
 
2.6%
동 37
 
2.5%
지 31
 
2.1%
시 29
 
2.0%
단 28
 
1.9%
Other values (242) 1041
70.8%
Decimal Number
ValueCountFrequency (%)
1 13
28.9%
2 12
26.7%
3 8
17.8%
0 4
 
8.9%
6 3
 
6.7%
5 2
 
4.4%
4 2
 
4.4%
7 1
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
e 4
40.0%
x 1
 
10.0%
l 1
 
10.0%
p 1
 
10.0%
c 1
 
10.0%
o 1
 
10.0%
m 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
D 3
23.1%
H 2
15.4%
L 2
15.4%
T 2
15.4%
K 2
15.4%
G 1
 
7.7%
B 1
 
7.7%
Space Separator
ValueCountFrequency (%)
83
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1471
90.2%
Common 137
 
8.4%
Latin 23
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 71
 
4.8%
대 70
 
4.8%
아 45
 
3.1%
스 43
 
2.9%
이 38
 
2.6%
신 38
 
2.6%
동 37
 
2.5%
지 31
 
2.1%
시 29
 
2.0%
단 28
 
1.9%
Other values (242) 1041
70.8%
Latin
ValueCountFrequency (%)
e 4
17.4%
D 3
13.0%
H 2
8.7%
L 2
8.7%
T 2
8.7%
K 2
8.7%
x 1
 
4.3%
l 1
 
4.3%
p 1
 
4.3%
c 1
 
4.3%
Other values (4) 4
17.4%
Common
ValueCountFrequency (%)
83
60.6%
1 13
 
9.5%
2 12
 
8.8%
3 8
 
5.8%
( 4
 
2.9%
0 4
 
2.9%
) 4
 
2.9%
6 3
 
2.2%
5 2
 
1.5%
4 2
 
1.5%
Other values (2) 2
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1471
90.2%
ASCII 160
 
9.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
83
51.9%
1 13
 
8.1%
2 12
 
7.5%
3 8
 
5.0%
e 4
 
2.5%
( 4
 
2.5%
0 4
 
2.5%
) 4
 
2.5%
6 3
 
1.9%
D 3
 
1.9%
Other values (16) 22
 
13.8%
Hangul
ValueCountFrequency (%)
구 71
 
4.8%
대 70
 
4.8%
아 45
 
3.1%
스 43
 
2.9%
이 38
 
2.6%
신 38
 
2.6%
동 37
 
2.5%
지 31
 
2.1%
시 29
 
2.0%
단 28
 
1.9%
Other values (242) 1041
70.8%

Interactions

2024-03-14T22:22:12.429251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:22:10.860374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:22:11.642715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:22:12.700889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:22:11.119741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:22:11.902090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:22:12.957544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:22:11.381484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:22:12.163934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T22:22:19.451936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번법정동본번부번
순번1.0000.3490.3160.000
법정동0.3491.0000.8550.000
본번0.3160.8551.0000.000
부번0.0000.0000.0001.000
2024-03-14T22:22:19.711146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번본번부번법정동
순번1.000-0.032-0.2040.129
본번-0.0321.000-0.3330.513
부번-0.204-0.3331.0000.000
법정동0.1290.5130.0001.000

Missing values

2024-03-14T22:22:13.301923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T22:22:13.651107image/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

순번시군구법정동본번부번건물명
01대구광역시 동구신천동15060신세계동대구복합환승센터
12대구광역시 동구율하동11170롯데쇼핑프라자
23대구광역시 동구신서동11881하우스디 어반 메가시티
34대구광역시 동구신암동57631대구 파티마병원
45대구광역시 동구봉무동15450롯데아울렛 이시아폴리스점
56대구광역시 동구신천동2945현대시티아울렛 대구점
67대구광역시 동구신서동11410한국가스공사
78대구광역시 동구신서동11870코스트코 대구혁신도시점
89대구광역시 동구신암동2940동대구역
910대구광역시 동구지저동4005대구국제공항
순번시군구법정동본번부번건물명
162163대구광역시 동구숙천동3860대구신서혁신엘에이치천년나무13단지
163164대구광역시 동구신암동67420동대구효성해링턴플레이스
164165대구광역시 동구신암동68027동대구해모로스퀘어웨스트
165166대구광역시 동구신암동25514동대구역화성파크드림
166167대구광역시 동구신암동811동대구해모로스퀘어이스트
167168대구광역시 동구율암동12970대구파라곤프레스티지
168169대구광역시 동구효목동6371동대구2차비스타동원
169170대구광역시 동구용계동4921용계역푸르지오아츠베르2단지
170171대구광역시 동구용계동57512용계역푸르지오아츠베르1단지
171172대구광역시 동구각산동3011주식회사신서랜드