Overview

Dataset statistics

Number of variables8
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory72.1 B

Variable types

Text2
Categorical3
Numeric2
DateTime1

Dataset

Description대구광역시 남구_자전거 공기주입기 현황_20230501
Author대구광역시 남구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15086178&dataSetDetailId=1508617819421967e918b&provdMethod=FILE

Alerts

주관부서 has constant value ""Constant
데이터기준일자 has constant value ""Constant
위도 is highly overall correlated with 수동 공기주입기 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 수동 공기주입기 and 1 other fieldsHigh correlation
수동 공기주입기 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
태양열 공기주입기 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위치 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-10 20:22:28.270792
Analysis finished2023-12-10 20:22:29.682573
Duration1.41 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

위치
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-11T05:22:29.909653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length9.875
Min length4

Characters and Unicode

Total characters316
Distinct characters95
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row대명3동 행정복지센터
2nd row앞산힐스테이트 맞은편
3rd row앞산힐스테이트 옆
4th row고산골 맨발산책로 입구
5th row봉덕로 오늘아침잡은소 가게 앞
ValueCountFrequency (%)
하단 5
 
7.6%
앞 4
 
6.1%
행정복지센터 3
 
4.5%
3번출구 3
 
4.5%
2번출구 3
 
4.5%
대명역 2
 
3.0%
서부정류장역 2
 
3.0%
영대병원역 2
 
3.0%
출구 2
 
3.0%
앞산힐스테이트 2
 
3.0%
Other values (37) 38
57.6%
2023-12-11T05:22:30.486511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35
 
11.1%
구 16
 
5.1%
역 15
 
4.7%
출 12
 
3.8%
번 12
 
3.8%
대 11
 
3.5%
지 8
 
2.5%
명 8
 
2.5%
산 7
 
2.2%
동 7
 
2.2%
Other values (85) 185
58.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 261
82.6%
Space Separator 35
 
11.1%
Decimal Number 16
 
5.1%
Open Punctuation 2
 
0.6%
Close Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 16
 
6.1%
역 15
 
5.7%
출 12
 
4.6%
번 12
 
4.6%
대 11
 
4.2%
지 8
 
3.1%
명 8
 
3.1%
산 7
 
2.7%
동 7
 
2.7%
정 6
 
2.3%
Other values (78) 159
60.9%
Decimal Number
ValueCountFrequency (%)
3 6
37.5%
4 4
25.0%
2 4
25.0%
1 2
 
12.5%
Space Separator
ValueCountFrequency (%)
35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 261
82.6%
Common 55
 
17.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 16
 
6.1%
역 15
 
5.7%
출 12
 
4.6%
번 12
 
4.6%
대 11
 
4.2%
지 8
 
3.1%
명 8
 
3.1%
산 7
 
2.7%
동 7
 
2.7%
정 6
 
2.3%
Other values (78) 159
60.9%
Common
ValueCountFrequency (%)
35
63.6%
3 6
 
10.9%
4 4
 
7.3%
2 4
 
7.3%
1 2
 
3.6%
( 2
 
3.6%
) 2
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 261
82.6%
ASCII 55
 
17.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35
63.6%
3 6
 
10.9%
4 4
 
7.3%
2 4
 
7.3%
1 2
 
3.6%
( 2
 
3.6%
) 2
 
3.6%
Hangul
ValueCountFrequency (%)
구 16
 
6.1%
역 15
 
5.7%
출 12
 
4.6%
번 12
 
4.6%
대 11
 
4.2%
지 8
 
3.1%
명 8
 
3.1%
산 7
 
2.7%
동 7
 
2.7%
정 6
 
2.3%
Other values (78) 159
60.9%

주소
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-11T05:22:30.809529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length17.15625
Min length14

Characters and Unicode

Total characters549
Distinct characters42
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

Unique32 ?
Unique (%)100.0%

Sample

1st row대구광역시 남구 명덕로20길 115
2nd row대구광역시 남구 대덕로 175
3rd row대구광역시 남구 대덕로30길 1
4th row대구광역시 남구 봉덕동 산129-1
5th row대구광역시 남구 봉덕로 35
ValueCountFrequency (%)
대구광역시 32
25.2%
남구 32
25.2%
대명동 6
 
4.7%
대명로 5
 
3.9%
봉덕동 4
 
3.1%
명덕로 2
 
1.6%
51 1
 
0.8%
1617-36 1
 
0.8%
332 1
 
0.8%
272 1
 
0.8%
Other values (42) 42
33.1%
2023-12-11T05:22:31.320948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
95
17.3%
구 64
11.7%
대 47
 
8.6%
광 32
 
5.8%
역 32
 
5.8%
시 32
 
5.8%
남 32
 
5.8%
1 27
 
4.9%
로 21
 
3.8%
2 19
 
3.5%
Other values (32) 148
27.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 332
60.5%
Decimal Number 113
 
20.6%
Space Separator 95
 
17.3%
Dash Punctuation 9
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 64
19.3%
대 47
14.2%
광 32
9.6%
역 32
9.6%
시 32
9.6%
남 32
9.6%
로 21
 
6.3%
명 16
 
4.8%
덕 11
 
3.3%
동 11
 
3.3%
Other values (20) 34
10.2%
Decimal Number
ValueCountFrequency (%)
1 27
23.9%
2 19
16.8%
3 14
12.4%
0 10
 
8.8%
6 10
 
8.8%
7 9
 
8.0%
5 8
 
7.1%
9 7
 
6.2%
8 6
 
5.3%
4 3
 
2.7%
Space Separator
ValueCountFrequency (%)
95
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 332
60.5%
Common 217
39.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 64
19.3%
대 47
14.2%
광 32
9.6%
역 32
9.6%
시 32
9.6%
남 32
9.6%
로 21
 
6.3%
명 16
 
4.8%
덕 11
 
3.3%
동 11
 
3.3%
Other values (20) 34
10.2%
Common
ValueCountFrequency (%)
95
43.8%
1 27
 
12.4%
2 19
 
8.8%
3 14
 
6.5%
0 10
 
4.6%
6 10
 
4.6%
- 9
 
4.1%
7 9
 
4.1%
5 8
 
3.7%
9 7
 
3.2%
Other values (2) 9
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 332
60.5%
ASCII 217
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
95
43.8%
1 27
 
12.4%
2 19
 
8.8%
3 14
 
6.5%
0 10
 
4.6%
6 10
 
4.6%
- 9
 
4.1%
7 9
 
4.1%
5 8
 
3.7%
9 7
 
3.2%
Other values (2) 9
 
4.1%
Hangul
ValueCountFrequency (%)
구 64
19.3%
대 47
14.2%
광 32
9.6%
역 32
9.6%
시 32
9.6%
남 32
9.6%
로 21
 
6.3%
명 16
 
4.8%
덕 11
 
3.3%
동 11
 
3.3%
Other values (20) 34
10.2%

수동 공기주입기
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
<NA>
27 
1
5 

Length

Max length4
Median length4
Mean length3.53125
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row1
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 27
84.4%
1 5
 
15.6%

Length

2023-12-11T05:22:31.552064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:22:31.712725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 27
84.4%
1 5
 
15.6%

태양열 공기주입기
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
1
27 
<NA>
5 

Length

Max length4
Median length1
Mean length1.46875
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row<NA>
5th row1

Common Values

ValueCountFrequency (%)
1 27
84.4%
<NA> 5
 
15.6%

Length

2023-12-11T05:22:31.875928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:22:32.040239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 27
84.4%
na 5
 
15.6%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.842954
Minimum35.826033
Maximum35.85668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T05:22:32.252613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.826033
5-th percentile35.833743
Q135.839106
median35.841324
Q335.845714
95-th percentile35.85525
Maximum35.85668
Range0.030647
Interquartile range (IQR)0.0066085

Descriptive statistics

Standard deviation0.0067932382
Coefficient of variation (CV)0.00018952786
Kurtosis0.53451269
Mean35.842954
Median Absolute Deviation (MAD)0.00362759
Skewness0.16771147
Sum1146.9745
Variance4.6148085 × 10-5
MonotonicityNot monotonic
2023-12-11T05:22:32.478570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
35.844325 2
 
6.2%
35.845627 1
 
3.1%
35.841138 1
 
3.1%
35.848486 1
 
3.1%
35.839107 1
 
3.1%
35.839101 1
 
3.1%
35.845975 1
 
3.1%
35.84466918 1
 
3.1%
35.839073 1
 
3.1%
35.841508 1
 
3.1%
Other values (21) 21
65.6%
ValueCountFrequency (%)
35.826033 1
3.1%
35.832498 1
3.1%
35.834761 1
3.1%
35.837044 1
3.1%
35.837375 1
3.1%
35.838241 1
3.1%
35.839073 1
3.1%
35.839101 1
3.1%
35.839107 1
3.1%
35.839238 1
3.1%
ValueCountFrequency (%)
35.85668 1
3.1%
35.855319 1
3.1%
35.855193 1
3.1%
35.854789 1
3.1%
35.851184 1
3.1%
35.848486 1
3.1%
35.847437 1
3.1%
35.845975 1
3.1%
35.845627 1
3.1%
35.845396 1
3.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.58604
Minimum128.55731
Maximum128.60674
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T05:22:32.713222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.55731
5-th percentile128.55799
Q1128.57426
median128.5887
Q3128.5983
95-th percentile128.60624
Maximum128.60674
Range0.049433
Interquartile range (IQR)0.02403925

Descriptive statistics

Standard deviation0.015005802
Coefficient of variation (CV)0.00011669854
Kurtosis-0.84030655
Mean128.58604
Median Absolute Deviation (MAD)0.0114505
Skewness-0.37567881
Sum4114.7531
Variance0.0002251741
MonotonicityNot monotonic
2023-12-11T05:22:32.951787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
128.588708 2
 
6.2%
128.598149 1
 
3.1%
128.602052 1
 
3.1%
128.57778 1
 
3.1%
128.573749 1
 
3.1%
128.573752 1
 
3.1%
128.569536 1
 
3.1%
128.5886891 1
 
3.1%
128.577393 1
 
3.1%
128.573462 1
 
3.1%
Other values (21) 21
65.6%
ValueCountFrequency (%)
128.557307 1
3.1%
128.557849 1
3.1%
128.558098 1
3.1%
128.565892 1
3.1%
128.569536 1
3.1%
128.573462 1
3.1%
128.573749 1
3.1%
128.573752 1
3.1%
128.574431 1
3.1%
128.577393 1
3.1%
ValueCountFrequency (%)
128.60674 1
3.1%
128.606269 1
3.1%
128.606208 1
3.1%
128.605523 1
3.1%
128.602996 1
3.1%
128.602052 1
3.1%
128.600294 1
3.1%
128.598515 1
3.1%
128.598229 1
3.1%
128.598149 1
3.1%

주관부서
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
대구광역시 남구 교통과
32 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구광역시 남구 교통과 32
100.0%

Length

2023-12-11T05:22:33.141135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T05:22:33.278047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 32
33.3%
남구 32
33.3%
교통과 32
33.3%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
Minimum2023-05-01 00:00:00
Maximum2023-05-01 00:00:00
2023-12-11T05:22:33.392762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:22:33.529889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T05:22:29.058013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:22:28.682895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:22:29.211474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:22:28.859335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T05:22:33.642656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위치주소위도경도
위치1.0001.0001.0001.000
주소1.0001.0001.0001.000
위도1.0001.0001.0000.481
경도1.0001.0000.4811.000
2023-12-11T05:22:33.777488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
태양열 공기주입기수동 공기주입기
태양열 공기주입기1.000NaN
수동 공기주입기NaN1.000
2023-12-11T05:22:33.929177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도수동 공기주입기태양열 공기주입기
위도1.0000.3191.0001.000
경도0.3191.0001.0001.000
수동 공기주입기1.0001.0001.0000.000
태양열 공기주입기1.0001.0000.0001.000

Missing values

2023-12-11T05:22:29.389710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T05:22:29.603745image/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

위치주소수동 공기주입기태양열 공기주입기위도경도주관부서데이터기준일자
0대명3동 행정복지센터대구광역시 남구 명덕로20길 115<NA>135.845627128.598149대구광역시 남구 교통과2023-05-01
1앞산힐스테이트 맞은편대구광역시 남구 대덕로 175<NA>135.841138128.602052대구광역시 남구 교통과2023-05-01
2앞산힐스테이트 옆대구광역시 남구 대덕로30길 1<NA>135.840746128.600294대구광역시 남구 교통과2023-05-01
3고산골 맨발산책로 입구대구광역시 남구 봉덕동 산129-11<NA>35.832498128.597372대구광역시 남구 교통과2023-05-01
4봉덕로 오늘아침잡은소 가게 앞대구광역시 남구 봉덕로 35<NA>135.845233128.594609대구광역시 남구 교통과2023-05-01
5중동교 하단대구광역시 남구 봉덕동 883-76<NA>135.842157128.60674대구광역시 남구 교통과2023-05-01
6희망교 하단대구광역시 남구 이천동 572-11<NA>35.847437128.606208대구광역시 남구 교통과2023-05-01
7서부정류장역 3번출구 동남편대구광역시 남구 월배로 496<NA>135.837375128.557849대구광역시 남구 교통과2023-05-01
8서부정류장역 3번출구 서부정류장 앞대구광역시 남구 대명동 1135-17<NA>135.837044128.557307대구광역시 남구 교통과2023-05-01
9성당네거리 성주막창 앞대구광역시 남구 성당로 2<NA>135.838241128.558098대구광역시 남구 교통과2023-05-01
위치주소수동 공기주입기태양열 공기주입기위도경도주관부서데이터기준일자
22남구청대구광역시 남구 이천로 51<NA>135.845396128.598229대구광역시 남구 교통과2023-05-01
23남구국민체육센터대구광역시 남구 앞산순환로 686<NA>135.844325128.588708대구광역시 남구 교통과2023-05-01
24교대역3번 출구대구광역시 남구 대명동 2009-10<NA>135.851184128.590799대구광역시 남구 교통과2023-05-01
25대명1동 행정복지센터대구광역시 남구 두류공원로 38<NA>135.841508128.573462대구광역시 남구 교통과2023-05-01
26안지랑네거리대구광역시 남구 대명로 177<NA>135.839073128.577393대구광역시 남구 교통과2023-05-01
27영대병원역 3번출구대구광역시 남구 대명동 64-10<NA>135.844669128.588689대구광역시 남구 교통과2023-05-01
28대명4동 행정복지센터대구광역시 남구 두류공원로20길 19<NA>135.845975128.569536대구광역시 남구 교통과2023-05-01
29대명역 2번출구대구광역시 남구 대명로 71<NA>135.839101128.573752대구광역시 남구 교통과2023-05-01
30안지랑역 2번출구대구광역시 남구 대명동 1291<NA>135.839107128.573749대구광역시 남구 교통과2023-05-01
31대명사회복지관대구광역시 남구 양지로 8<NA>135.848486128.57778대구광역시 남구 교통과2023-05-01