Overview

Dataset statistics

Number of variables15
Number of observations54
Missing cells161
Missing cells (%)19.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 KiB
Average record size in memory127.4 B

Variable types

Text3
Numeric3
Unsupported2
Boolean3
Categorical4

Dataset

Description대구광역시_남구_자전거보관소
Author대구광역시 남구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15017318&dataSetDetailId=150173181f77dead15226&provdMethod=FILE

Alerts

수리대설치여부 has constant value ""Constant
관리기관전화번호 has constant value ""Constant
관리기관명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
공기주입기비치여부 is highly overall correlated with 공기주입기유형High correlation
공기주입기유형 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 공기주입기유형High correlation
소재지도로명주소 has 15 (27.8%) missing valuesMissing
소재지지번주소 has 38 (70.4%) missing valuesMissing
설치년도 has 54 (100.0%) missing valuesMissing
설치형태 has 54 (100.0%) missing valuesMissing
자전거보관소명 has unique valuesUnique
설치년도 is an unsupported type, check if it needs cleaning or further analysisUnsupported
설치형태 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-21 02:17:20.995983
Analysis finished2024-04-21 02:17:25.175992
Duration4.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자전거보관소명
Text

UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size560.0 B
2024-04-21T11:17:25.873006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length19
Mean length13.425926
Min length6

Characters and Unicode

Total characters725
Distinct characters150
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)100.0%

Sample

1st row성당못역 3번출구 서부정류장 앞
2nd row성당못역 3번출구 동남편
3rd row대명역 3번출구
4th row현충로역 4번출구 (동강한의원 앞)
5th row현충로역 3번출구 (앞산안마 앞)
ValueCountFrequency (%)
앞 26
 
15.6%
3번출구 7
 
4.2%
주민센터 7
 
4.2%
입구 5
 
3.0%
하단 5
 
3.0%
옆 4
 
2.4%
현충로역 4
 
2.4%
고산골 4
 
2.4%
안지랑역 3
 
1.8%
4번출구 3
 
1.8%
Other values (81) 99
59.3%
2024-04-21T11:17:26.856046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
113
 
15.6%
앞 30
 
4.1%
구 30
 
4.1%
대 21
 
2.9%
역 19
 
2.6%
) 18
 
2.5%
( 18
 
2.5%
동 18
 
2.5%
번 17
 
2.3%
출 16
 
2.2%
Other values (140) 425
58.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 540
74.5%
Space Separator 113
 
15.6%
Decimal Number 31
 
4.3%
Close Punctuation 18
 
2.5%
Open Punctuation 18
 
2.5%
Uppercase Letter 4
 
0.6%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 30
 
5.6%
구 30
 
5.6%
대 21
 
3.9%
역 19
 
3.5%
동 18
 
3.3%
번 17
 
3.1%
출 16
 
3.0%
명 12
 
2.2%
교 12
 
2.2%
주 12
 
2.2%
Other values (124) 353
65.4%
Decimal Number
ValueCountFrequency (%)
3 10
32.3%
1 7
22.6%
2 6
19.4%
4 3
 
9.7%
0 2
 
6.5%
6 1
 
3.2%
5 1
 
3.2%
9 1
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
E 1
25.0%
M 1
25.0%
H 1
25.0%
N 1
25.0%
Space Separator
ValueCountFrequency (%)
113
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 540
74.5%
Common 181
 
25.0%
Latin 4
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 30
 
5.6%
구 30
 
5.6%
대 21
 
3.9%
역 19
 
3.5%
동 18
 
3.3%
번 17
 
3.1%
출 16
 
3.0%
명 12
 
2.2%
교 12
 
2.2%
주 12
 
2.2%
Other values (124) 353
65.4%
Common
ValueCountFrequency (%)
113
62.4%
) 18
 
9.9%
( 18
 
9.9%
3 10
 
5.5%
1 7
 
3.9%
2 6
 
3.3%
4 3
 
1.7%
0 2
 
1.1%
6 1
 
0.6%
- 1
 
0.6%
Other values (2) 2
 
1.1%
Latin
ValueCountFrequency (%)
E 1
25.0%
M 1
25.0%
H 1
25.0%
N 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 540
74.5%
ASCII 185
 
25.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
113
61.1%
) 18
 
9.7%
( 18
 
9.7%
3 10
 
5.4%
1 7
 
3.8%
2 6
 
3.2%
4 3
 
1.6%
0 2
 
1.1%
6 1
 
0.5%
- 1
 
0.5%
Other values (6) 6
 
3.2%
Hangul
ValueCountFrequency (%)
앞 30
 
5.6%
구 30
 
5.6%
대 21
 
3.9%
역 19
 
3.5%
동 18
 
3.3%
번 17
 
3.1%
출 16
 
3.0%
명 12
 
2.2%
교 12
 
2.2%
주 12
 
2.2%
Other values (124) 353
65.4%

소재지도로명주소
Text

MISSING 

Distinct39
Distinct (%)100.0%
Missing15
Missing (%)27.8%
Memory size560.0 B
2024-04-21T11:17:27.569861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length16.487179
Min length14

Characters and Unicode

Total characters643
Distinct characters47
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

Unique39 ?
Unique (%)100.0%

Sample

1st row대구광역시 남구 월배로 496
2nd row대구광역시 남구 대명로 70
3rd row대구광역시 남구 대명로220
4th row대구광역시 남구 대명로 214
5th row대구광역시 남구 대명로 213
ValueCountFrequency (%)
대구광역시 39
25.2%
남구 39
25.2%
대명로 6
 
3.9%
중앙대로 5
 
3.2%
명덕로 4
 
2.6%
봉덕로 4
 
2.6%
213 2
 
1.3%
이천로 2
 
1.3%
대명로220 1
 
0.6%
175 1
 
0.6%
Other values (52) 52
33.5%
2024-04-21T11:17:28.514661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
116
18.0%
구 78
12.1%
대 56
 
8.7%
광 39
 
6.1%
역 39
 
6.1%
시 39
 
6.1%
남 39
 
6.1%
로 34
 
5.3%
2 27
 
4.2%
1 25
 
3.9%
Other values (37) 151
23.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 409
63.6%
Space Separator 116
 
18.0%
Decimal Number 116
 
18.0%
Dash Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 78
19.1%
대 56
13.7%
광 39
9.5%
역 39
9.5%
시 39
9.5%
남 39
9.5%
로 34
8.3%
명 15
 
3.7%
길 12
 
2.9%
덕 12
 
2.9%
Other values (25) 46
11.2%
Decimal Number
ValueCountFrequency (%)
2 27
23.3%
1 25
21.6%
5 13
11.2%
3 12
10.3%
4 10
 
8.6%
6 8
 
6.9%
0 7
 
6.0%
8 5
 
4.3%
9 5
 
4.3%
7 4
 
3.4%
Space Separator
ValueCountFrequency (%)
116
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 409
63.6%
Common 234
36.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 78
19.1%
대 56
13.7%
광 39
9.5%
역 39
9.5%
시 39
9.5%
남 39
9.5%
로 34
8.3%
명 15
 
3.7%
길 12
 
2.9%
덕 12
 
2.9%
Other values (25) 46
11.2%
Common
ValueCountFrequency (%)
116
49.6%
2 27
 
11.5%
1 25
 
10.7%
5 13
 
5.6%
3 12
 
5.1%
4 10
 
4.3%
6 8
 
3.4%
0 7
 
3.0%
8 5
 
2.1%
9 5
 
2.1%
Other values (2) 6
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 409
63.6%
ASCII 234
36.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
116
49.6%
2 27
 
11.5%
1 25
 
10.7%
5 13
 
5.6%
3 12
 
5.1%
4 10
 
4.3%
6 8
 
3.4%
0 7
 
3.0%
8 5
 
2.1%
9 5
 
2.1%
Other values (2) 6
 
2.6%
Hangul
ValueCountFrequency (%)
구 78
19.1%
대 56
13.7%
광 39
9.5%
역 39
9.5%
시 39
9.5%
남 39
9.5%
로 34
8.3%
명 15
 
3.7%
길 12
 
2.9%
덕 12
 
2.9%
Other values (25) 46
11.2%

소재지지번주소
Text

MISSING 

Distinct15
Distinct (%)93.8%
Missing38
Missing (%)70.4%
Memory size560.0 B
2024-04-21T11:17:29.017153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length19.125
Min length17

Characters and Unicode

Total characters306
Distinct characters25
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

Unique14 ?
Unique (%)87.5%

Sample

1st row대구광역시 남구 대명동 1135-17
2nd row대구광역시 남구 대명동 1684-3
3rd row대구광역시 남구 대명동 336-1
4th row대구광역시 남구 대명동 148-29
5th row대구광역시 남구 대명동 1623-60
ValueCountFrequency (%)
대구광역시 16
24.6%
남구 16
24.6%
대명동 9
13.8%
봉덕동 6
 
9.2%
1671-36 2
 
3.1%
산 1
 
1.5%
572-1 1
 
1.5%
이천동 1
 
1.5%
883-76 1
 
1.5%
산129-1 1
 
1.5%
Other values (11) 11
16.9%
2024-04-21T11:17:29.767490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49
16.0%
구 32
 
10.5%
대 25
 
8.2%
1 21
 
6.9%
남 16
 
5.2%
동 16
 
5.2%
시 16
 
5.2%
역 16
 
5.2%
광 16
 
5.2%
- 15
 
4.9%
Other values (15) 84
27.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 162
52.9%
Decimal Number 80
26.1%
Space Separator 49
 
16.0%
Dash Punctuation 15
 
4.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 32
19.8%
대 25
15.4%
남 16
9.9%
동 16
9.9%
시 16
9.9%
역 16
9.9%
광 16
9.9%
명 9
 
5.6%
봉 6
 
3.7%
덕 6
 
3.7%
Other values (3) 4
 
2.5%
Decimal Number
ValueCountFrequency (%)
1 21
26.2%
6 11
13.8%
3 10
12.5%
2 8
 
10.0%
0 8
 
10.0%
8 6
 
7.5%
7 5
 
6.2%
4 5
 
6.2%
9 4
 
5.0%
5 2
 
2.5%
Space Separator
ValueCountFrequency (%)
49
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 162
52.9%
Common 144
47.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 32
19.8%
대 25
15.4%
남 16
9.9%
동 16
9.9%
시 16
9.9%
역 16
9.9%
광 16
9.9%
명 9
 
5.6%
봉 6
 
3.7%
덕 6
 
3.7%
Other values (3) 4
 
2.5%
Common
ValueCountFrequency (%)
49
34.0%
1 21
14.6%
- 15
 
10.4%
6 11
 
7.6%
3 10
 
6.9%
2 8
 
5.6%
0 8
 
5.6%
8 6
 
4.2%
7 5
 
3.5%
4 5
 
3.5%
Other values (2) 6
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 162
52.9%
ASCII 144
47.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49
34.0%
1 21
14.6%
- 15
 
10.4%
6 11
 
7.6%
3 10
 
6.9%
2 8
 
5.6%
0 8
 
5.6%
8 6
 
4.2%
7 5
 
3.5%
4 5
 
3.5%
Other values (2) 6
 
4.2%
Hangul
ValueCountFrequency (%)
구 32
19.8%
대 25
15.4%
남 16
9.9%
동 16
9.9%
시 16
9.9%
역 16
9.9%
광 16
9.9%
명 9
 
5.6%
봉 6
 
3.7%
덕 6
 
3.7%
Other values (3) 4
 
2.5%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.843635
Minimum35.826033
Maximum35.858764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-04-21T11:17:30.003479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.826033
5-th percentile35.832774
Q135.839092
median35.842081
Q335.850436
95-th percentile35.85571
Maximum35.858764
Range0.0327311
Interquartile range (IQR)0.01134395

Descriptive statistics

Standard deviation0.007601814
Coefficient of variation (CV)0.00021208267
Kurtosis-0.55958111
Mean35.843635
Median Absolute Deviation (MAD)0.00430065
Skewness0.10415649
Sum1935.5563
Variance5.7787576 × 10-5
MonotonicityNot monotonic
2024-04-21T11:17:30.263848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.834761 2
 
3.7%
35.837044 1
 
1.9%
35.841285 1
 
1.9%
35.8432344 1
 
1.9%
35.8552846 1
 
1.9%
35.84002858 1
 
1.9%
35.832923 1
 
1.9%
35.8440849 1
 
1.9%
35.8495281 1
 
1.9%
35.8338982 1
 
1.9%
Other values (43) 43
79.6%
ValueCountFrequency (%)
35.826033 1
1.9%
35.8300041 1
1.9%
35.832498 1
1.9%
35.832923 1
1.9%
35.83308885 1
1.9%
35.8338982 1
1.9%
35.834761 2
3.7%
35.8367855 1
1.9%
35.837044 1
1.9%
35.837231 1
1.9%
ValueCountFrequency (%)
35.8587641 1
1.9%
35.856761 1
1.9%
35.856501 1
1.9%
35.8552846 1
1.9%
35.855224 1
1.9%
35.854789 1
1.9%
35.8538161 1
1.9%
35.8537053 1
1.9%
35.853071 1
1.9%
35.8524011 1
1.9%

경도
Real number (ℝ)

Distinct53
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.58863
Minimum128.55731
Maximum128.60683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-04-21T11:17:30.529587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.55731
5-th percentile128.56288
Q1128.57946
median128.59058
Q3128.59969
95-th percentile128.60576
Maximum128.60683
Range0.049519
Interquartile range (IQR)0.020233175

Descriptive statistics

Standard deviation0.013651929
Coefficient of variation (CV)0.00010616746
Kurtosis-0.384877
Mean128.58863
Median Absolute Deviation (MAD)0.0097389
Skewness-0.65479199
Sum6943.7861
Variance0.00018637516
MonotonicityNot monotonic
2024-04-21T11:17:30.784926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.605523 2
 
3.7%
128.557307 1
 
1.9%
128.606826 1
 
1.9%
128.587828 1
 
1.9%
128.586457 1
 
1.9%
128.5689557 1
 
1.9%
128.585007 1
 
1.9%
128.5908376 1
 
1.9%
128.5736995 1
 
1.9%
128.604992 1
 
1.9%
Other values (43) 43
79.6%
ValueCountFrequency (%)
128.557307 1
1.9%
128.5577515 1
1.9%
128.5583832 1
1.9%
128.5653048 1
1.9%
128.565496 1
1.9%
128.5689557 1
1.9%
128.5735803 1
1.9%
128.5736995 1
1.9%
128.5737281 1
1.9%
128.5744147 1
1.9%
ValueCountFrequency (%)
128.606826 1
1.9%
128.6063206 1
1.9%
128.606208 1
1.9%
128.605523 2
3.7%
128.604992 1
1.9%
128.604116 1
1.9%
128.603449 1
1.9%
128.6030706 1
1.9%
128.602996 1
1.9%
128.6021112 1
1.9%

보관대수
Real number (ℝ)

Distinct19
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.740741
Minimum4
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-04-21T11:17:30.999091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q17.25
median14
Q321
95-th percentile55.3
Maximum100
Range96
Interquartile range (IQR)13.75

Descriptive statistics

Standard deviation20.338814
Coefficient of variation (CV)1.0302964
Kurtosis7.7170435
Mean19.740741
Median Absolute Deviation (MAD)7
Skewness2.7030285
Sum1066
Variance413.66737
MonotonicityNot monotonic
2024-04-21T11:17:31.199672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
14 15
27.8%
7 9
16.7%
10 5
 
9.3%
5 4
 
7.4%
21 3
 
5.6%
27 2
 
3.7%
40 2
 
3.7%
42 2
 
3.7%
12 2
 
3.7%
26 1
 
1.9%
Other values (9) 9
16.7%
ValueCountFrequency (%)
4 1
 
1.9%
5 4
 
7.4%
7 9
16.7%
8 1
 
1.9%
10 5
 
9.3%
12 2
 
3.7%
13 1
 
1.9%
14 15
27.8%
21 3
 
5.6%
26 1
 
1.9%
ValueCountFrequency (%)
100 1
1.9%
95 1
1.9%
80 1
1.9%
42 2
3.7%
40 2
3.7%
34 1
1.9%
30 1
1.9%
28 1
1.9%
27 2
3.7%
26 1
1.9%

설치년도
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing54
Missing (%)100.0%
Memory size614.0 B

설치형태
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing54
Missing (%)100.0%
Memory size614.0 B

차양막설치여부
Boolean

Distinct2
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size182.0 B
False
48 
True
6 
ValueCountFrequency (%)
False 48
88.9%
True 6
 
11.1%
2024-04-21T11:17:31.370796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

공기주입기비치여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size182.0 B
False
31 
True
23 
ValueCountFrequency (%)
False 31
57.4%
True 23
42.6%
2024-04-21T11:17:31.513378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

공기주입기유형
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size560.0 B
<NA>
31 
태양광식
18 
수동식
4 
태양광식+수동식
 
1

Length

Max length8
Median length4
Mean length4
Min length3

Unique

Unique1 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 31
57.4%
태양광식 18
33.3%
수동식 4
 
7.4%
태양광식+수동식 1
 
1.9%

Length

2024-04-21T11:17:31.690316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:17:31.868663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 31
57.4%
태양광식 18
33.3%
수동식 4
 
7.4%
태양광식+수동식 1
 
1.9%

수리대설치여부
Boolean

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size182.0 B
False
54 
ValueCountFrequency (%)
False 54
100.0%
2024-04-21T11:17:32.029811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

관리기관전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size560.0 B
053-664-3018
54 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row053-664-3018
2nd row053-664-3018
3rd row053-664-3018
4th row053-664-3018
5th row053-664-3018

Common Values

ValueCountFrequency (%)
053-664-3018 54
100.0%

Length

2024-04-21T11:17:32.194058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:17:32.393301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
053-664-3018 54
100.0%

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size560.0 B
대구광역시 남구청
54 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구광역시 남구청 54
100.0%

Length

2024-04-21T11:17:32.631768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:17:32.789649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 54
50.0%
남구청 54
50.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size560.0 B
2020-09-02
54 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-09-02
2nd row2020-09-02
3rd row2020-09-02
4th row2020-09-02
5th row2020-09-02

Common Values

ValueCountFrequency (%)
2020-09-02 54
100.0%

Length

2024-04-21T11:17:32.955739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:17:33.114767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-09-02 54
100.0%

Interactions

2024-04-21T11:17:23.143837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:21.655860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:22.416192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:23.342815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:21.918854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:22.671256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:23.479905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:22.166022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:17:22.907743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:17:33.223390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자전거보관소명소재지도로명주소소재지지번주소위도경도보관대수차양막설치여부공기주입기비치여부공기주입기유형
자전거보관소명1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.000NaN1.0001.0001.0001.0001.0001.000
소재지지번주소1.000NaN1.0001.0001.0000.8921.0001.0001.000
위도1.0001.0001.0001.0000.5050.5430.4080.0000.969
경도1.0001.0001.0000.5051.0000.0000.0000.2530.000
보관대수1.0001.0000.8920.5430.0001.0000.3040.5010.648
차양막설치여부1.0001.0001.0000.4080.0000.3041.0000.0000.000
공기주입기비치여부1.0001.0001.0000.0000.2530.5010.0001.000NaN
공기주입기유형1.0001.0001.0000.9690.0000.6480.000NaN1.000
2024-04-21T11:17:33.436088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차양막설치여부공기주입기비치여부공기주입기유형
차양막설치여부1.0000.0000.000
공기주입기비치여부0.0001.0001.000
공기주입기유형0.0001.0001.000
2024-04-21T11:17:33.591881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도보관대수차양막설치여부공기주입기비치여부공기주입기유형
위도1.0000.094-0.1220.2830.0000.656
경도0.0941.0000.0880.2910.2290.000
보관대수-0.1220.0881.0000.2060.3450.297
차양막설치여부0.2830.2910.2061.0000.0000.000
공기주입기비치여부0.0000.2290.3450.0001.0001.000
공기주입기유형0.6560.0000.2970.0001.0001.000

Missing values

2024-04-21T11:17:24.035511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:17:24.638131image/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.
2024-04-21T11:17:25.017516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

자전거보관소명소재지도로명주소소재지지번주소위도경도보관대수설치년도설치형태차양막설치여부공기주입기비치여부공기주입기유형수리대설치여부관리기관전화번호관리기관명데이터기준일자
0성당못역 3번출구 서부정류장 앞<NA>대구광역시 남구 대명동 1135-1735.837044128.55730726<NA><NA>YY태양광식N053-664-3018대구광역시 남구청2020-09-02
1성당못역 3번출구 동남편대구광역시 남구 월배로 496<NA>35.836785128.55775142<NA><NA>YY태양광식N053-664-3018대구광역시 남구청2020-09-02
2대명역 3번출구대구광역시 남구 대명로 70<NA>35.838952128.56530510<NA><NA>NN<NA>N053-664-3018대구광역시 남구청2020-09-02
3현충로역 4번출구 (동강한의원 앞)<NA>대구광역시 남구 대명동 1684-335.841139128.58112314<NA><NA>NY태양광식N053-664-3018대구광역시 남구청2020-09-02
4현충로역 3번출구 (앞산안마 앞)대구광역시 남구 대명로220대구광역시 남구 대명동 336-135.840566128.58129621<NA><NA>NY수동식N053-664-3018대구광역시 남구청2020-09-02
5현충로역 2번출구 (진설옥설렁탕 앞)대구광역시 남구 대명로 214<NA>35.840315128.58081614<NA><NA>NN<NA>N053-664-3018대구광역시 남구청2020-09-02
6현충로역 1번출구대구광역시 남구 대명로 213<NA>35.840732128.58041114<NA><NA>NY태양광식N053-664-3018대구광역시 남구청2020-09-02
7영대병원역 2번출구 (스피드중고차상사 앞)<NA>대구광역시 남구 대명동 148-2935.844325128.58870842<NA><NA>NY태양광식N053-664-3018대구광역시 남구청2020-09-02
8영대병원역 3번출구 (명덕시장 입구 앞)대구광역시 남구 대명로 299-1<NA>35.844766128.58874414<NA><NA>NN<NA>N053-664-3018대구광역시 남구청2020-09-02
9대명역 4번출구<NA>대구광역시 남구 대명동 1623-6035.839566128.56549614<NA><NA>NY태양광식N053-664-3018대구광역시 남구청2020-09-02
자전거보관소명소재지도로명주소소재지지번주소위도경도보관대수설치년도설치형태차양막설치여부공기주입기비치여부공기주입기유형수리대설치여부관리기관전화번호관리기관명데이터기준일자
44앞산힐스테이트 맞은편 (영동건업 앞)대구광역시 남구 대덕로 175<NA>35.841138128.6021117<NA><NA>NY태양광식N053-664-3018대구광역시 남구청2020-09-02
45봉덕로 ME만남의 집 앞(카페봉덕 앞)대구광역시 남구 봉덕로 45<NA>35.845324128.5955977<NA><NA>NN<NA>N053-664-3018대구광역시 남구청2020-09-02
46봉덕로 오늘아침잡은 소 앞대구광역시 남구 봉덕로 35<NA>35.845233128.5946097<NA><NA>NY태양광식N053-664-3018대구광역시 남구청2020-09-02
47남구보건소 앞대구광역시 남구 영선길 34<NA>35.853816128.59183310<NA><NA>YN<NA>N053-664-3018대구광역시 남구청2020-09-02
48명덕역 하단대구광역시 남구 중앙대로 261<NA>35.856761128.59016380<NA><NA>NY태양광식N053-664-3018대구광역시 남구청2020-09-02
49건들바위역 하단대구광역시 남구 명덕로 272<NA>35.855224128.59978140<NA><NA>NY태양광식N053-664-3018대구광역시 남구청2020-09-02
50대봉교역 하단대구광역시 남구 명덕로 332<NA>35.854789128.60632140<NA><NA>NY수동식N053-664-3018대구광역시 남구청2020-09-02
51대명1동 주민센터대구광역시 남구 두류공원로 38<NA>35.84173128.5735810<NA><NA>NN<NA>N053-664-3018대구광역시 남구청2020-09-02
52남구국민체육센터대구광역시 남구 앞산순환로 686<NA>35.833089128.6001818<NA><NA>YY태양광식N053-664-3018대구광역시 남구청2020-09-02
53봉덕시장 공연장대구광역시 남구 봉덕로27길 16-15<NA>35.84558128.60307112<NA><NA>NN<NA>N053-664-3018대구광역시 남구청2020-09-02