Overview

Dataset statistics

Number of variables10
Number of observations1039
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory85.4 KiB
Average record size in memory84.1 B

Variable types

Numeric4
Categorical3
Text3

Dataset

Description파일 다운로드
Author양천구
URLhttps://data.seoul.go.kr/dataList/OA-22036/F/1/datasetView.do

Alerts

기준일 has constant value ""Constant
연번 is highly overall correlated with 용도High correlation
위도 is highly overall correlated with 동별High correlation
경도 is highly overall correlated with 동별High correlation
용도 is highly overall correlated with 연번High correlation
동별 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
연번 has unique valuesUnique
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-11 08:01:33.598060
Analysis finished2023-12-11 08:01:36.661928
Duration3.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1039
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean520
Minimum1
Maximum1039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-12-11T17:01:36.752145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile52.9
Q1260.5
median520
Q3779.5
95-th percentile987.1
Maximum1039
Range1038
Interquartile range (IQR)519

Descriptive statistics

Standard deviation300.07777
Coefficient of variation (CV)0.57707263
Kurtosis-1.2
Mean520
Median Absolute Deviation (MAD)260
Skewness0
Sum540280
Variance90046.667
MonotonicityStrictly increasing
2023-12-11T17:01:36.914691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
684 1
 
0.1%
686 1
 
0.1%
687 1
 
0.1%
688 1
 
0.1%
689 1
 
0.1%
690 1
 
0.1%
691 1
 
0.1%
692 1
 
0.1%
693 1
 
0.1%
Other values (1029) 1029
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1039 1
0.1%
1038 1
0.1%
1037 1
0.1%
1036 1
0.1%
1035 1
0.1%
1034 1
0.1%
1033 1
0.1%
1032 1
0.1%
1031 1
0.1%
1030 1
0.1%

용도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
방범
685 
어린이보호
228 
공원
126 

Length

Max length5
Median length2
Mean length2.6583253
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
방범 685
65.9%
어린이보호 228
 
21.9%
공원 126
 
12.1%

Length

2023-12-11T17:01:37.086429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T17:01:37.217709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
방범 685
65.9%
어린이보호 228
 
21.9%
공원 126
 
12.1%

동별
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
신정3동
118 
신정4동
112 
목2동
98 
목4동
71 
신월1동
69 
Other values (13)
571 

Length

Max length4
Median length4
Mean length3.6987488
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row목1동
2nd row목1동
3rd row목1동
4th row목1동
5th row목5동

Common Values

ValueCountFrequency (%)
신정3동 118
11.4%
신정4동 112
 
10.8%
목2동 98
 
9.4%
목4동 71
 
6.8%
신월1동 69
 
6.6%
신월7동 62
 
6.0%
목3동 59
 
5.7%
신월3동 59
 
5.7%
신월2동 54
 
5.2%
신월5동 47
 
4.5%
Other values (8) 290
27.9%

Length

2023-12-11T17:01:37.334208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신정3동 118
11.4%
신정4동 112
 
10.8%
목2동 98
 
9.4%
목4동 71
 
6.8%
신월1동 69
 
6.6%
신월7동 62
 
6.0%
목3동 59
 
5.7%
신월3동 59
 
5.7%
신월2동 54
 
5.2%
신정7동 47
 
4.5%
Other values (8) 290
27.9%

관리번호
Text

UNIQUE 

Distinct1039
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
2023-12-11T17:01:37.610836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.3051011
Min length4

Characters and Unicode

Total characters5512
Distinct characters19
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

Unique1039 ?
Unique (%)100.0%

Sample

1st row목001
2nd row목002
3rd row목003
4th row목004
5th row목005
ValueCountFrequency (%)
목001 1
 
0.1%
고신정057 1
 
0.1%
어목017 1
 
0.1%
어목004 1
 
0.1%
어목005 1
 
0.1%
어목006 1
 
0.1%
어목007 1
 
0.1%
어목008 1
 
0.1%
어목009 1
 
0.1%
어목010 1
 
0.1%
Other values (1029) 1029
99.0%
2023-12-11T17:01:38.146302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1170
21.2%
신 635
11.5%
1 380
 
6.9%
월 326
 
5.9%
정 309
 
5.6%
고 307
 
5.6%
목 279
 
5.1%
어 233
 
4.2%
2 224
 
4.1%
3 220
 
4.0%
Other values (9) 1429
25.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3145
57.1%
Other Letter 2339
42.4%
Dash Punctuation 28
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1170
37.2%
1 380
 
12.1%
2 224
 
7.1%
3 220
 
7.0%
4 213
 
6.8%
5 203
 
6.5%
6 196
 
6.2%
7 190
 
6.0%
8 180
 
5.7%
9 169
 
5.4%
Other Letter
ValueCountFrequency (%)
신 635
27.1%
월 326
13.9%
정 309
13.2%
고 307
13.1%
목 279
11.9%
어 233
 
10.0%
공 125
 
5.3%
원 125
 
5.3%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3173
57.6%
Hangul 2339
42.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1170
36.9%
1 380
 
12.0%
2 224
 
7.1%
3 220
 
6.9%
4 213
 
6.7%
5 203
 
6.4%
6 196
 
6.2%
7 190
 
6.0%
8 180
 
5.7%
9 169
 
5.3%
Hangul
ValueCountFrequency (%)
신 635
27.1%
월 326
13.9%
정 309
13.2%
고 307
13.1%
목 279
11.9%
어 233
 
10.0%
공 125
 
5.3%
원 125
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3173
57.6%
Hangul 2339
42.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1170
36.9%
1 380
 
12.0%
2 224
 
7.1%
3 220
 
6.9%
4 213
 
6.7%
5 203
 
6.4%
6 196
 
6.2%
7 190
 
6.0%
8 180
 
5.7%
9 169
 
5.3%
Hangul
ValueCountFrequency (%)
신 635
27.1%
월 326
13.9%
정 309
13.2%
고 307
13.1%
목 279
11.9%
어 233
 
10.0%
공 125
 
5.3%
원 125
 
5.3%

구주소
Text

Distinct956
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
2023-12-11T17:01:38.688730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length9.0298364
Min length5

Characters and Unicode

Total characters9382
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

Unique898 ?
Unique (%)86.4%

Sample

1st row목동 917-9
2nd row목동 406-35
3rd row목동 809-1
4th row목동 933
5th row목동 902
ValueCountFrequency (%)
신정동 373
 
17.9%
신월동 351
 
16.9%
목동 311
 
15.0%
320 7
 
0.3%
1321-11 5
 
0.2%
906 4
 
0.2%
231-187 4
 
0.2%
199-51 4
 
0.2%
1286 4
 
0.2%
1316 4
 
0.2%
Other values (944) 1011
48.7%
2023-12-11T17:01:39.358122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1050
11.2%
1045
11.1%
동 1038
11.1%
- 911
 
9.7%
신 727
 
7.7%
2 590
 
6.3%
3 452
 
4.8%
9 443
 
4.7%
정 374
 
4.0%
5 374
 
4.0%
Other values (15) 2378
25.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4609
49.1%
Other Letter 2817
30.0%
Space Separator 1045
 
11.1%
Dash Punctuation 911
 
9.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1038
36.8%
신 727
25.8%
정 374
 
13.3%
월 353
 
12.5%
목 313
 
11.1%
산 4
 
0.1%
교 2
 
0.1%
오 1
 
< 0.1%
아 1
 
< 0.1%
래 1
 
< 0.1%
Other values (3) 3
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 1050
22.8%
2 590
12.8%
3 452
9.8%
9 443
9.6%
5 374
 
8.1%
0 370
 
8.0%
7 368
 
8.0%
4 358
 
7.8%
6 319
 
6.9%
8 285
 
6.2%
Space Separator
ValueCountFrequency (%)
1045
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 911
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6565
70.0%
Hangul 2817
30.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1038
36.8%
신 727
25.8%
정 374
 
13.3%
월 353
 
12.5%
목 313
 
11.1%
산 4
 
0.1%
교 2
 
0.1%
오 1
 
< 0.1%
아 1
 
< 0.1%
래 1
 
< 0.1%
Other values (3) 3
 
0.1%
Common
ValueCountFrequency (%)
1 1050
16.0%
1045
15.9%
- 911
13.9%
2 590
9.0%
3 452
6.9%
9 443
6.7%
5 374
 
5.7%
0 370
 
5.6%
7 368
 
5.6%
4 358
 
5.5%
Other values (2) 604
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6565
70.0%
Hangul 2817
30.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1050
16.0%
1045
15.9%
- 911
13.9%
2 590
9.0%
3 452
6.9%
9 443
6.7%
5 374
 
5.7%
0 370
 
5.6%
7 368
 
5.6%
4 358
 
5.5%
Other values (2) 604
9.2%
Hangul
ValueCountFrequency (%)
동 1038
36.8%
신 727
25.8%
정 374
 
13.3%
월 353
 
12.5%
목 313
 
11.1%
산 4
 
0.1%
교 2
 
0.1%
오 1
 
< 0.1%
아 1
 
< 0.1%
래 1
 
< 0.1%
Other values (3) 3
 
0.1%

상세주소
Text

Distinct461
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
2023-12-11T17:01:39.771431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length29
Mean length7.2463908
Min length1

Characters and Unicode

Total characters7529
Distinct characters350
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique425 ?
Unique (%)40.9%

Sample

1st row목동41타워 앞
2nd row이디아 커피숖 앞
3rd row목동타운빌딩 베스킨라빈스 앞
4th row삼익아파트 102동 목동세계로약국 앞
5th row아파트 2단지 201동 앞
ValueCountFrequency (%)
앞 307
 
14.3%
사거리 266
 
12.4%
삼거리 260
 
12.1%
건물 121
 
5.6%
인도 74
 
3.4%
옆 64
 
3.0%
도로 56
 
2.6%
입구 36
 
1.7%
안 35
 
1.6%
전봇대 29
 
1.4%
Other values (513) 899
41.9%
2023-12-11T17:01:40.477824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1170
 
15.5%
리 569
 
7.6%
거 539
 
7.2%
앞 319
 
4.2%
사 295
 
3.9%
삼 269
 
3.6%
원 178
 
2.4%
도 170
 
2.3%
공 162
 
2.2%
건 134
 
1.8%
Other values (340) 3724
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6032
80.1%
Space Separator 1170
 
15.5%
Decimal Number 264
 
3.5%
Open Punctuation 15
 
0.2%
Close Punctuation 15
 
0.2%
Uppercase Letter 12
 
0.2%
Dash Punctuation 9
 
0.1%
Other Punctuation 5
 
0.1%
Lowercase Letter 5
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 569
 
9.4%
거 539
 
8.9%
앞 319
 
5.3%
사 295
 
4.9%
삼 269
 
4.5%
원 178
 
3.0%
도 170
 
2.8%
공 162
 
2.7%
건 134
 
2.2%
로 131
 
2.2%
Other values (311) 3266
54.1%
Decimal Number
ValueCountFrequency (%)
1 74
28.0%
0 58
22.0%
2 39
14.8%
3 27
 
10.2%
4 18
 
6.8%
5 17
 
6.4%
6 10
 
3.8%
7 9
 
3.4%
9 8
 
3.0%
8 4
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
A 3
25.0%
G 2
16.7%
S 2
16.7%
U 2
16.7%
C 2
16.7%
I 1
 
8.3%
Lowercase Letter
ValueCountFrequency (%)
u 1
20.0%
c 1
20.0%
k 1
20.0%
s 1
20.0%
m 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
. 1
 
20.0%
Space Separator
ValueCountFrequency (%)
1170
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6032
80.1%
Common 1480
 
19.7%
Latin 17
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 569
 
9.4%
거 539
 
8.9%
앞 319
 
5.3%
사 295
 
4.9%
삼 269
 
4.5%
원 178
 
3.0%
도 170
 
2.8%
공 162
 
2.7%
건 134
 
2.2%
로 131
 
2.2%
Other values (311) 3266
54.1%
Common
ValueCountFrequency (%)
1170
79.1%
1 74
 
5.0%
0 58
 
3.9%
2 39
 
2.6%
3 27
 
1.8%
4 18
 
1.2%
5 17
 
1.1%
( 15
 
1.0%
) 15
 
1.0%
6 10
 
0.7%
Other values (8) 37
 
2.5%
Latin
ValueCountFrequency (%)
A 3
17.6%
G 2
11.8%
S 2
11.8%
U 2
11.8%
C 2
11.8%
u 1
 
5.9%
c 1
 
5.9%
k 1
 
5.9%
s 1
 
5.9%
I 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6032
80.1%
ASCII 1497
 
19.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1170
78.2%
1 74
 
4.9%
0 58
 
3.9%
2 39
 
2.6%
3 27
 
1.8%
4 18
 
1.2%
5 17
 
1.1%
( 15
 
1.0%
) 15
 
1.0%
6 10
 
0.7%
Other values (19) 54
 
3.6%
Hangul
ValueCountFrequency (%)
리 569
 
9.4%
거 539
 
8.9%
앞 319
 
5.3%
사 295
 
4.9%
삼 269
 
4.5%
원 178
 
3.0%
도 170
 
2.8%
공 162
 
2.7%
건 134
 
2.2%
로 131
 
2.2%
Other values (311) 3266
54.1%

카메라수
Real number (ℝ)

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.267565
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-12-11T17:01:40.662541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1059556
Coefficient of variation (CV)0.33846475
Kurtosis-0.53666994
Mean3.267565
Median Absolute Deviation (MAD)1
Skewness-0.24761536
Sum3395
Variance1.2231377
MonotonicityNot monotonic
2023-12-11T17:01:40.830834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 348
33.5%
4 310
29.8%
2 165
15.9%
5 140
13.5%
1 75
 
7.2%
6 1
 
0.1%
ValueCountFrequency (%)
1 75
 
7.2%
2 165
15.9%
3 348
33.5%
4 310
29.8%
5 140
13.5%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 140
13.5%
4 310
29.8%
3 348
33.5%
2 165
15.9%
1 75
 
7.2%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct1008
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.527418
Minimum37.5018
Maximum37.550829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-12-11T17:01:41.024841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.5018
5-th percentile37.510692
Q137.519775
median37.525839
Q337.536192
95-th percentile37.54529
Maximum37.550829
Range0.049029
Interquartile range (IQR)0.0164169

Descriptive statistics

Standard deviation0.010703107
Coefficient of variation (CV)0.00028520767
Kurtosis-0.91640017
Mean37.527418
Median Absolute Deviation (MAD)0.008281
Skewness0.12740192
Sum38990.987
Variance0.00011455651
MonotonicityNot monotonic
2023-12-11T17:01:41.238361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.526253 4
 
0.4%
37.53166 2
 
0.2%
37.51982 2
 
0.2%
37.516578 2
 
0.2%
37.532108 2
 
0.2%
37.52304 2
 
0.2%
37.536205 2
 
0.2%
37.54402975 2
 
0.2%
37.51793 2
 
0.2%
37.5253 2
 
0.2%
Other values (998) 1017
97.9%
ValueCountFrequency (%)
37.5018 1
0.1%
37.5048976 1
0.1%
37.5055 1
0.1%
37.505562 1
0.1%
37.5058721 1
0.1%
37.505885 1
0.1%
37.50594 1
0.1%
37.506587 1
0.1%
37.50687 1
0.1%
37.5069227 1
0.1%
ValueCountFrequency (%)
37.550829 1
0.1%
37.5504274 1
0.1%
37.549662 1
0.1%
37.5493052 1
0.1%
37.549211 1
0.1%
37.548807 1
0.1%
37.5485319 1
0.1%
37.548492 1
0.1%
37.54804 1
0.1%
37.5479404 1
0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct1006
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.85382
Minimum126.82303
Maximum126.888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-12-11T17:01:41.447121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.82303
5-th percentile126.82826
Q1126.83766
median126.85526
Q3126.86818
95-th percentile126.8771
Maximum126.888
Range0.0649737
Interquartile range (IQR)0.0305213

Descriptive statistics

Standard deviation0.01672032
Coefficient of variation (CV)0.00013180778
Kurtosis-1.2871001
Mean126.85382
Median Absolute Deviation (MAD)0.014656
Skewness-0.11638267
Sum131801.12
Variance0.00027956912
MonotonicityNot monotonic
2023-12-11T17:01:41.643337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.840702 4
 
0.4%
126.866184 2
 
0.2%
126.873519 2
 
0.2%
126.83715 2
 
0.2%
126.86094 2
 
0.2%
126.847919 2
 
0.2%
126.837173 2
 
0.2%
126.834611 2
 
0.2%
126.835805 2
 
0.2%
126.864059 2
 
0.2%
Other values (996) 1017
97.9%
ValueCountFrequency (%)
126.82303 1
0.1%
126.823878 1
0.1%
126.824084 1
0.1%
126.82413 1
0.1%
126.824303 1
0.1%
126.824464 1
0.1%
126.824478 1
0.1%
126.824656 2
0.2%
126.824703 1
0.1%
126.824755 1
0.1%
ValueCountFrequency (%)
126.8880037 1
0.1%
126.88697 1
0.1%
126.886469 1
0.1%
126.88623 1
0.1%
126.885742 1
0.1%
126.8851549 1
0.1%
126.8847513 1
0.1%
126.8843423 1
0.1%
126.8842203 1
0.1%
126.884017 1
0.1%

기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
2022-08-22
1039 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-08-22
2nd row2022-08-22
3rd row2022-08-22
4th row2022-08-22
5th row2022-08-22

Common Values

ValueCountFrequency (%)
2022-08-22 1039
100.0%

Length

2023-12-11T17:01:41.848656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T17:01:41.958637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-08-22 1039
100.0%

Interactions

2023-12-11T17:01:35.903155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:01:34.334234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:01:34.900092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:01:35.404039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:01:36.037625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:01:34.459650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:01:35.037140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:01:35.508065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:01:36.145505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:01:34.571612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:01:35.153507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:01:35.624344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:01:36.259314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:01:34.713057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:01:35.259785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:01:35.746619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:01:42.047423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번용도동별카메라수위도경도
연번1.0000.9270.7310.3540.6310.710
용도0.9271.0000.4020.5880.2790.128
동별0.7310.4021.0000.2430.8690.897
카메라수0.3540.5880.2431.0000.1730.036
위도0.6310.2790.8690.1731.0000.651
경도0.7100.1280.8970.0360.6511.000
2023-12-11T17:01:42.199854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동별용도
동별1.0000.201
용도0.2011.000
2023-12-11T17:01:42.334970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번카메라수위도경도용도동별
연번1.000-0.158-0.466-0.1600.8970.385
카메라수-0.1581.0000.089-0.0030.2950.098
위도-0.4660.0891.0000.2020.1730.570
경도-0.160-0.0030.2021.0000.0760.626
용도0.8970.2950.1730.0761.0000.201
동별0.3850.0980.5700.6260.2011.000

Missing values

2023-12-11T17:01:36.415884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:01:36.576183image/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

연번용도동별관리번호구주소상세주소카메라수위도경도기준일
01방범목1동목001목동 917-9목동41타워 앞537.527618126.8765282022-08-22
12방범목1동목002목동 406-35이디아 커피숖 앞437.52503126.873212022-08-22
23방범목1동목003목동 809-1목동타운빌딩 베스킨라빈스 앞537.52756126.864222022-08-22
34방범목1동목004목동 933삼익아파트 102동 목동세계로약국 앞537.522262126.8772252022-08-22
45방범목5동목005목동 902아파트 2단지 201동 앞437.536178126.8775812022-08-22
56방범목5동목006목동 909-9목동능력교회 앞537.536514126.8832112022-08-22
67방범목5동목007목동 199-66월촌초등학교 앞 교차로437.539722126.8754832022-08-22
78방범목2동목008목동 231-98GS25 편의점 앞537.541094126.8713282022-08-22
89방범목2동목009목동 536스마일랄인마트 앞437.544742126.8743152022-08-22
910방범목3동목010목동 324-188한아름할인마트 앞537.543582126.8642632022-08-22
연번용도동별관리번호구주소상세주소카메라수위도경도기준일
10291030공원신정7동공원113신정동 162-56갈산공원 01337.508252126.8687312022-08-22
10301031공원신정7동공원114신정동 162-56갈산공원 02337.50798126.8684462022-08-22
10311032공원신정3동공원115신정동 695-6계남근린공원01237.513521126.8514542022-08-22
10321033공원신정3동공원116신정동 695-6계남근린공원02337.513405126.851532022-08-22
10331034공원신월1동공원117신월동 227-4곰달래마을마당237.529778126.8372022-08-22
10341035공원목3동공원118목동 324-10목3동 소공원237.544249126.8648232022-08-22
10351036공원신월2동공원119신월동 459신곡어린이공원237.526264126.8448922022-08-22
10361037공원신정1동공원120신정동 1017-5해태어린이공원237.521633126.8620622022-08-22
10371038공원목2동공원121목동 199-51용왕산 근린공원02237.542288126.8740472022-08-22
10381039공원신정3동공원122신정동 767넓은들어린이공원 안337.511105126.8394012022-08-22