Overview

Dataset statistics

Number of variables18
Number of observations52
Missing cells6
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory151.5 B

Variable types

Numeric5
Categorical7
Text5
Boolean1

Dataset

Description서울특별시 성동구에서 제공하는 어린이보호구역(연번, 시도, 자치구명, 동명, 도로명주소, 지번주소, 위도, 경도, 시설명 등)에 대한 정보입니다.
Author서울특별시 성동구
URLhttps://www.data.go.kr/data/15111326/fileData.do

Alerts

시도 has constant value ""Constant
자치구명 has constant value ""Constant
CCTV설치여부 has constant value ""Constant
데이터기준일자 has constant value ""Constant
관할경찰서명 is highly overall correlated with 연번 and 7 other fieldsHigh correlation
시설종류 is highly overall correlated with 지정일자 and 2 other fieldsHigh correlation
동명 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
관리기관명 is highly overall correlated with 연번 and 7 other fieldsHigh correlation
연번 is highly overall correlated with 동명 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 관리기관명 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 동명 and 2 other fieldsHigh correlation
지정일자 is highly overall correlated with CCTV설치대수 and 3 other fieldsHigh correlation
CCTV설치대수 is highly overall correlated with 지정일자 and 2 other fieldsHigh correlation
관리기관명 is highly imbalanced (86.3%)Imbalance
관할경찰서명 is highly imbalanced (86.3%)Imbalance
지번주소 has 1 (1.9%) missing valuesMissing
위도 has 1 (1.9%) missing valuesMissing
경도 has 1 (1.9%) missing valuesMissing
CCTV설치여부 has 1 (1.9%) missing valuesMissing
CCTV설치대수 has 1 (1.9%) missing valuesMissing
보호구역도로폭 has 1 (1.9%) missing valuesMissing
연번 has unique valuesUnique
코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 10:05:50.575659
Analysis finished2023-12-12 10:05:55.427558
Duration4.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.5
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T19:05:55.535137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.55
Q113.75
median26.5
Q339.25
95-th percentile49.45
Maximum52
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.57187763
Kurtosis-1.2
Mean26.5
Median Absolute Deviation (MAD)13
Skewness0
Sum1378
Variance229.66667
MonotonicityStrictly increasing
2023-12-12T19:05:55.784008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.9%
28 1
 
1.9%
30 1
 
1.9%
31 1
 
1.9%
32 1
 
1.9%
33 1
 
1.9%
34 1
 
1.9%
35 1
 
1.9%
36 1
 
1.9%
37 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1 1
1.9%
2 1
1.9%
3 1
1.9%
4 1
1.9%
5 1
1.9%
6 1
1.9%
7 1
1.9%
8 1
1.9%
9 1
1.9%
10 1
1.9%
ValueCountFrequency (%)
52 1
1.9%
51 1
1.9%
50 1
1.9%
49 1
1.9%
48 1
1.9%
47 1
1.9%
46 1
1.9%
45 1
1.9%
44 1
1.9%
43 1
1.9%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
서울특별시
52 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 52
100.0%

Length

2023-12-12T19:05:55.992872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:05:56.093909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 52
100.0%

자치구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
성동구
52 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
성동구 52
100.0%

Length

2023-12-12T19:05:56.208844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:05:56.340094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
성동구 52
100.0%

동명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Memory size548.0 B
마장동
6 
옥수동
5 
행당동
5 
금호4가동
4 
성수동1가
4 
Other values (16)
28 

Length

Max length7
Median length3
Mean length3.9423077
Min length3

Unique

Unique8 ?
Unique (%)15.4%

Sample

1st row금호1가동
2nd row금호1가동
3rd row금호2.3가동
4th row금호3가동
5th row금호4가동

Common Values

ValueCountFrequency (%)
마장동 6
11.5%
옥수동 5
 
9.6%
행당동 5
 
9.6%
금호4가동 4
 
7.7%
성수동1가 4
 
7.7%
홍익동 3
 
5.8%
용답동 3
 
5.8%
사근동 3
 
5.8%
왕십리2동 3
 
5.8%
금호1가동 2
 
3.8%
Other values (11) 14
26.9%

Length

2023-12-12T19:05:56.487988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
마장동 6
11.5%
행당동 5
 
9.6%
옥수동 5
 
9.6%
금호4가동 4
 
7.7%
성수동1가 4
 
7.7%
홍익동 3
 
5.8%
용답동 3
 
5.8%
사근동 3
 
5.8%
왕십리2동 3
 
5.8%
응봉동 2
 
3.8%
Other values (11) 14
26.9%

도로명주소
Text

Distinct45
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Memory size548.0 B
2023-12-12T19:05:56.835422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length32
Mean length20.384615
Min length16

Characters and Unicode

Total characters1060
Distinct characters100
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)73.1%

Sample

1st row서울특별시 성동구 행당로1길 13
2nd row서울특별시 성동구 난계로 18
3rd row서울특별시 성동구 무수막길 69
4th row서울특별시 성동구 금호산2길 35
5th row서울특별시 성동구 독서당로 251
ValueCountFrequency (%)
서울특별시 52
23.2%
성동구 52
23.2%
독서당로 4
 
1.8%
21 3
 
1.3%
9 3
 
1.3%
34 3
 
1.3%
22 3
 
1.3%
한림말길 3
 
1.3%
16 2
 
0.9%
금호로 2
 
0.9%
Other values (83) 97
43.3%
2023-12-12T19:05:57.347420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
172
16.2%
동 64
 
6.0%
서 58
 
5.5%
성 56
 
5.3%
시 53
 
5.0%
특 52
 
4.9%
별 52
 
4.9%
구 52
 
4.9%
울 52
 
4.9%
1 38
 
3.6%
Other values (90) 411
38.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 708
66.8%
Space Separator 172
 
16.2%
Decimal Number 165
 
15.6%
Other Punctuation 10
 
0.9%
Dash Punctuation 3
 
0.3%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 64
 
9.0%
서 58
 
8.2%
성 56
 
7.9%
시 53
 
7.5%
특 52
 
7.3%
별 52
 
7.3%
구 52
 
7.3%
울 52
 
7.3%
길 37
 
5.2%
로 36
 
5.1%
Other values (75) 196
27.7%
Decimal Number
ValueCountFrequency (%)
1 38
23.0%
2 31
18.8%
3 25
15.2%
4 16
9.7%
5 14
 
8.5%
6 13
 
7.9%
9 11
 
6.7%
0 8
 
4.8%
8 6
 
3.6%
7 3
 
1.8%
Space Separator
ValueCountFrequency (%)
172
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 708
66.8%
Common 352
33.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 64
 
9.0%
서 58
 
8.2%
성 56
 
7.9%
시 53
 
7.5%
특 52
 
7.3%
별 52
 
7.3%
구 52
 
7.3%
울 52
 
7.3%
길 37
 
5.2%
로 36
 
5.1%
Other values (75) 196
27.7%
Common
ValueCountFrequency (%)
172
48.9%
1 38
 
10.8%
2 31
 
8.8%
3 25
 
7.1%
4 16
 
4.5%
5 14
 
4.0%
6 13
 
3.7%
9 11
 
3.1%
. 10
 
2.8%
0 8
 
2.3%
Other values (5) 14
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 708
66.8%
ASCII 352
33.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
172
48.9%
1 38
 
10.8%
2 31
 
8.8%
3 25
 
7.1%
4 16
 
4.5%
5 14
 
4.0%
6 13
 
3.7%
9 11
 
3.1%
. 10
 
2.8%
0 8
 
2.3%
Other values (5) 14
 
4.0%
Hangul
ValueCountFrequency (%)
동 64
 
9.0%
서 58
 
8.2%
성 56
 
7.9%
시 53
 
7.5%
특 52
 
7.3%
별 52
 
7.3%
구 52
 
7.3%
울 52
 
7.3%
길 37
 
5.2%
로 36
 
5.1%
Other values (75) 196
27.7%

지번주소
Text

MISSING 

Distinct44
Distinct (%)86.3%
Missing1
Missing (%)1.9%
Memory size548.0 B
2023-12-12T19:05:57.616518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.156863
Min length16

Characters and Unicode

Total characters977
Distinct characters44
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

Unique37 ?
Unique (%)72.5%

Sample

1st row서울특별시 성동구 금호동1가 48-2
2nd row서울특별시 성동구 금호동1가 622-1
3rd row서울특별시 성동구 금호동2가 511
4th row서울특별시 성동구 금호동3가 165-1
5th row서울특별시 성동구 금호동4가 1254
ValueCountFrequency (%)
서울특별시 51
25.0%
성동구 51
25.0%
마장동 6
 
2.9%
옥수동 5
 
2.5%
하왕십리동 5
 
2.5%
행당동 4
 
2.0%
성수동1가 4
 
2.0%
사근동 4
 
2.0%
금호동4가 4
 
2.0%
금호동1가 3
 
1.5%
Other values (51) 67
32.8%
2023-12-12T19:05:58.076874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
153
15.7%
동 102
 
10.4%
성 58
 
5.9%
서 51
 
5.2%
울 51
 
5.2%
구 51
 
5.2%
시 51
 
5.2%
별 51
 
5.2%
특 51
 
5.2%
1 42
 
4.3%
Other values (34) 316
32.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 590
60.4%
Decimal Number 205
 
21.0%
Space Separator 153
 
15.7%
Dash Punctuation 29
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 102
17.3%
성 58
9.8%
서 51
8.6%
울 51
8.6%
구 51
8.6%
시 51
8.6%
별 51
8.6%
특 51
8.6%
가 17
 
2.9%
수 12
 
2.0%
Other values (22) 95
16.1%
Decimal Number
ValueCountFrequency (%)
1 42
20.5%
2 34
16.6%
5 21
10.2%
6 19
9.3%
4 17
8.3%
3 17
8.3%
0 17
8.3%
8 14
 
6.8%
9 13
 
6.3%
7 11
 
5.4%
Space Separator
ValueCountFrequency (%)
153
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 590
60.4%
Common 387
39.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 102
17.3%
성 58
9.8%
서 51
8.6%
울 51
8.6%
구 51
8.6%
시 51
8.6%
별 51
8.6%
특 51
8.6%
가 17
 
2.9%
수 12
 
2.0%
Other values (22) 95
16.1%
Common
ValueCountFrequency (%)
153
39.5%
1 42
 
10.9%
2 34
 
8.8%
- 29
 
7.5%
5 21
 
5.4%
6 19
 
4.9%
4 17
 
4.4%
3 17
 
4.4%
0 17
 
4.4%
8 14
 
3.6%
Other values (2) 24
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 590
60.4%
ASCII 387
39.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
153
39.5%
1 42
 
10.9%
2 34
 
8.8%
- 29
 
7.5%
5 21
 
5.4%
6 19
 
4.9%
4 17
 
4.4%
3 17
 
4.4%
0 17
 
4.4%
8 14
 
3.6%
Other values (2) 24
 
6.2%
Hangul
ValueCountFrequency (%)
동 102
17.3%
성 58
9.8%
서 51
8.6%
울 51
8.6%
구 51
8.6%
시 51
8.6%
별 51
8.6%
특 51
8.6%
가 17
 
2.9%
수 12
 
2.0%
Other values (22) 95
16.1%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)98.0%
Missing1
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean37.554996
Minimum37.539414
Maximum37.570223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T19:05:58.256143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.539414
5-th percentile37.540436
Q137.54553
median37.556223
Q337.56268
95-th percentile37.568216
Maximum37.570223
Range0.03080932
Interquartile range (IQR)0.017150685

Descriptive statistics

Standard deviation0.009597068
Coefficient of variation (CV)0.00025554704
Kurtosis-1.3144996
Mean37.554996
Median Absolute Deviation (MAD)0.00902833
Skewness-0.10794648
Sum1915.3048
Variance9.2103715 × 10-5
MonotonicityNot monotonic
2023-12-12T19:05:58.444947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.56812285 2
 
3.8%
37.557465 1
 
1.9%
37.5623123 1
 
1.9%
37.54262971 1
 
1.9%
37.54230893 1
 
1.9%
37.5404159 1
 
1.9%
37.56370023 1
 
1.9%
37.5614705 1
 
1.9%
37.56910834 1
 
1.9%
37.56781934 1
 
1.9%
Other values (40) 40
76.9%
ValueCountFrequency (%)
37.5394137 1
1.9%
37.5404159 1
1.9%
37.54043604 1
1.9%
37.54043667 1
1.9%
37.5409419 1
1.9%
37.54230893 1
1.9%
37.54230974 1
1.9%
37.54262971 1
1.9%
37.54382386 1
1.9%
37.5438253 1
1.9%
ValueCountFrequency (%)
37.57022302 1
1.9%
37.56910834 1
1.9%
37.56830949 1
1.9%
37.56812285 2
3.8%
37.56783247 1
1.9%
37.56781934 1
1.9%
37.566426 1
1.9%
37.56624296 1
1.9%
37.56525103 1
1.9%
37.56512898 1
1.9%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)98.0%
Missing1
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean127.03605
Minimum127.01001
Maximum127.06953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T19:05:58.625326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.01001
5-th percentile127.01567
Q1127.02423
median127.03383
Q3127.04757
95-th percentile127.06062
Maximum127.06953
Range0.0595156
Interquartile range (IQR)0.0233409

Descriptive statistics

Standard deviation0.015209055
Coefficient of variation (CV)0.00011972236
Kurtosis-0.66033756
Mean127.03605
Median Absolute Deviation (MAD)0.0124687
Skewness0.37073753
Sum6478.8385
Variance0.00023131536
MonotonicityNot monotonic
2023-12-12T19:05:58.807837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0351082 2
 
3.8%
127.023576 1
 
1.9%
127.055149 1
 
1.9%
127.0155564 1
 
1.9%
127.0157872 1
 
1.9%
127.012722 1
 
1.9%
127.0291054 1
 
1.9%
127.0306547 1
 
1.9%
127.0326723 1
 
1.9%
127.0278897 1
 
1.9%
Other values (40) 40
76.9%
ValueCountFrequency (%)
127.0100124 1
1.9%
127.012722 1
1.9%
127.0155564 1
1.9%
127.0157855 1
1.9%
127.0157872 1
1.9%
127.0176529 1
1.9%
127.017941 1
1.9%
127.0181532 1
1.9%
127.0197434 1
1.9%
127.0218696 1
1.9%
ValueCountFrequency (%)
127.069528 1
1.9%
127.0695262 1
1.9%
127.0631674 1
1.9%
127.0580689 1
1.9%
127.0580668 1
1.9%
127.0563986 1
1.9%
127.055149 1
1.9%
127.0537611 1
1.9%
127.0502634 1
1.9%
127.050074 1
1.9%

시설명
Text

Distinct51
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size548.0 B
2023-12-12T19:05:59.114429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length3.9423077
Min length2

Characters and Unicode

Total characters205
Distinct characters76
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

Unique50 ?
Unique (%)96.2%

Sample

1st row금북
2nd row구립금일
3rd row금호
4th row금호유치원
5th row금옥
ValueCountFrequency (%)
마장 2
 
3.8%
꿈터 1
 
1.9%
구립용답 1
 
1.9%
옥정 1
 
1.9%
옥수삼성유치원 1
 
1.9%
옥정병설 1
 
1.9%
연꽃(국공립 1
 
1.9%
무학 1
 
1.9%
무학유치원 1
 
1.9%
솔유치원 1
 
1.9%
Other values (41) 41
78.8%
2023-12-12T19:05:59.557652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 16
 
7.8%
치 14
 
6.8%
유 14
 
6.8%
수 7
 
3.4%
병 6
 
2.9%
설 6
 
2.9%
동 6
 
2.9%
립 6
 
2.9%
성 6
 
2.9%
옥 6
 
2.9%
Other values (66) 118
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 195
95.1%
Open Punctuation 5
 
2.4%
Close Punctuation 5
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 16
 
8.2%
치 14
 
7.2%
유 14
 
7.2%
수 7
 
3.6%
병 6
 
3.1%
설 6
 
3.1%
동 6
 
3.1%
립 6
 
3.1%
성 6
 
3.1%
옥 6
 
3.1%
Other values (64) 108
55.4%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 195
95.1%
Common 10
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 16
 
8.2%
치 14
 
7.2%
유 14
 
7.2%
수 7
 
3.6%
병 6
 
3.1%
설 6
 
3.1%
동 6
 
3.1%
립 6
 
3.1%
성 6
 
3.1%
옥 6
 
3.1%
Other values (64) 108
55.4%
Common
ValueCountFrequency (%)
( 5
50.0%
) 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 195
95.1%
ASCII 10
 
4.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 16
 
8.2%
치 14
 
7.2%
유 14
 
7.2%
수 7
 
3.6%
병 6
 
3.1%
설 6
 
3.1%
동 6
 
3.1%
립 6
 
3.1%
성 6
 
3.1%
옥 6
 
3.1%
Other values (64) 108
55.4%
ASCII
ValueCountFrequency (%)
( 5
50.0%
) 5
50.0%

코드
Text

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
2023-12-12T19:05:59.832660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length1.5
Mean length1.5
Min length1

Characters and Unicode

Total characters78
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)100.0%

Sample

1st rowA
2nd rowI
3rd rowC
4th rowD
5th rowE
ValueCountFrequency (%)
a 1
 
1.9%
i 1
 
1.9%
an 1
 
1.9%
ad 1
 
1.9%
ae 1
 
1.9%
ag 1
 
1.9%
af 1
 
1.9%
ah 1
 
1.9%
ai 1
 
1.9%
aj 1
 
1.9%
Other values (42) 42
80.8%
2023-12-12T19:06:00.640906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 28
35.9%
I 2
 
2.6%
V 2
 
2.6%
X 2
 
2.6%
Z 2
 
2.6%
Y 2
 
2.6%
W 2
 
2.6%
U 2
 
2.6%
S 2
 
2.6%
T 2
 
2.6%
Other values (16) 32
41.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 78
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 28
35.9%
I 2
 
2.6%
V 2
 
2.6%
X 2
 
2.6%
Z 2
 
2.6%
Y 2
 
2.6%
W 2
 
2.6%
U 2
 
2.6%
S 2
 
2.6%
T 2
 
2.6%
Other values (16) 32
41.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 28
35.9%
I 2
 
2.6%
V 2
 
2.6%
X 2
 
2.6%
Z 2
 
2.6%
Y 2
 
2.6%
W 2
 
2.6%
U 2
 
2.6%
S 2
 
2.6%
T 2
 
2.6%
Other values (16) 32
41.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 28
35.9%
I 2
 
2.6%
V 2
 
2.6%
X 2
 
2.6%
Z 2
 
2.6%
Y 2
 
2.6%
W 2
 
2.6%
U 2
 
2.6%
S 2
 
2.6%
T 2
 
2.6%
Other values (16) 32
41.0%

시설종류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size548.0 B
초등학교
21 
유치원
12 
어린이집
11 
병설유치원
8 

Length

Max length5
Median length4
Mean length3.9230769
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row초등학교
2nd row어린이집
3rd row초등학교
4th row유치원
5th row초등학교

Common Values

ValueCountFrequency (%)
초등학교 21
40.4%
유치원 12
23.1%
어린이집 11
21.2%
병설유치원 8
 
15.4%

Length

2023-12-12T19:06:00.849486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:06:01.010906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
초등학교 21
40.4%
유치원 12
23.1%
어린이집 11
21.2%
병설유치원 8
 
15.4%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.3077
Minimum1995
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T19:06:01.153052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1995
5-th percentile1995
Q11997
median2009
Q32010
95-th percentile2016.45
Maximum2020
Range25
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.2397012
Coefficient of variation (CV)0.0036102695
Kurtosis-1.20466
Mean2005.3077
Median Absolute Deviation (MAD)3
Skewness-0.13279104
Sum104276
Variance52.413273
MonotonicityNot monotonic
2023-12-12T19:06:01.349531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2010 11
21.2%
1997 7
13.5%
2009 7
13.5%
1995 5
9.6%
1996 4
 
7.7%
2011 4
 
7.7%
2006 4
 
7.7%
2017 2
 
3.8%
1998 2
 
3.8%
2016 2
 
3.8%
Other values (4) 4
 
7.7%
ValueCountFrequency (%)
1995 5
9.6%
1996 4
 
7.7%
1997 7
13.5%
1998 2
 
3.8%
2002 1
 
1.9%
2005 1
 
1.9%
2006 4
 
7.7%
2008 1
 
1.9%
2009 7
13.5%
2010 11
21.2%
ValueCountFrequency (%)
2020 1
 
1.9%
2017 2
 
3.8%
2016 2
 
3.8%
2011 4
 
7.7%
2010 11
21.2%
2009 7
13.5%
2008 1
 
1.9%
2006 4
 
7.7%
2005 1
 
1.9%
2002 1
 
1.9%

관리기관명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
서울특별시 성동구
51 
<NA>
 
1

Length

Max length9
Median length9
Mean length8.9038462
Min length4

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st row서울특별시 성동구
2nd row서울특별시 성동구
3rd row서울특별시 성동구
4th row서울특별시 성동구
5th row서울특별시 성동구

Common Values

ValueCountFrequency (%)
서울특별시 성동구 51
98.1%
<NA> 1
 
1.9%

Length

2023-12-12T19:06:01.533496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:06:01.674361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 51
49.5%
성동구 51
49.5%
na 1
 
1.0%

관할경찰서명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
성동경찰서
51 
<NA>
 
1

Length

Max length5
Median length5
Mean length4.9807692
Min length4

Unique

Unique1 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
성동경찰서 51
98.1%
<NA> 1
 
1.9%

Length

2023-12-12T19:06:01.815648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:06:01.959354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
성동경찰서 51
98.1%
na 1
 
1.9%

CCTV설치여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)2.0%
Missing1
Missing (%)1.9%
Memory size236.0 B
True
51 
(Missing)
 
1
ValueCountFrequency (%)
True 51
98.1%
(Missing) 1
 
1.9%
2023-12-12T19:06:02.064198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

CCTV설치대수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)17.6%
Missing1
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean2.627451
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-12T19:06:02.179395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33.5
95-th percentile7
Maximum10
Range9
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.4816187
Coefficient of variation (CV)0.94449667
Kurtosis0.46791776
Mean2.627451
Median Absolute Deviation (MAD)0
Skewness1.3127407
Sum134
Variance6.1584314
MonotonicityNot monotonic
2023-12-12T19:06:02.324225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 31
59.6%
6 5
 
9.6%
3 4
 
7.7%
7 4
 
7.7%
2 3
 
5.8%
10 1
 
1.9%
4 1
 
1.9%
8 1
 
1.9%
5 1
 
1.9%
(Missing) 1
 
1.9%
ValueCountFrequency (%)
1 31
59.6%
2 3
 
5.8%
3 4
 
7.7%
4 1
 
1.9%
5 1
 
1.9%
6 5
 
9.6%
7 4
 
7.7%
8 1
 
1.9%
10 1
 
1.9%
ValueCountFrequency (%)
10 1
 
1.9%
8 1
 
1.9%
7 4
 
7.7%
6 5
 
9.6%
5 1
 
1.9%
4 1
 
1.9%
3 4
 
7.7%
2 3
 
5.8%
1 31
59.6%

보호구역도로폭
Text

MISSING 

Distinct29
Distinct (%)56.9%
Missing1
Missing (%)1.9%
Memory size548.0 B
2023-12-12T19:06:02.517810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.627451
Min length1

Characters and Unicode

Total characters134
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)37.3%

Sample

1st row5~20
2nd row8~9
3rd row6
4th row4
5th row30
ValueCountFrequency (%)
7 7
 
13.7%
8 4
 
7.8%
4~8 3
 
5.9%
24 3
 
5.9%
6~8.5 3
 
5.9%
4 3
 
5.9%
12 3
 
5.9%
8~10 2
 
3.9%
4~9 2
 
3.9%
4~13 2
 
3.9%
Other values (19) 19
37.3%
2023-12-12T19:06:02.892871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
~ 25
18.7%
1 17
12.7%
8 16
11.9%
4 15
11.2%
2 15
11.2%
5 10
 
7.5%
0 9
 
6.7%
7 8
 
6.0%
6 8
 
6.0%
. 4
 
3.0%
Other values (2) 7
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 105
78.4%
Math Symbol 25
 
18.7%
Other Punctuation 4
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17
16.2%
8 16
15.2%
4 15
14.3%
2 15
14.3%
5 10
9.5%
0 9
8.6%
7 8
7.6%
6 8
7.6%
9 4
 
3.8%
3 3
 
2.9%
Math Symbol
ValueCountFrequency (%)
~ 25
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 134
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
~ 25
18.7%
1 17
12.7%
8 16
11.9%
4 15
11.2%
2 15
11.2%
5 10
 
7.5%
0 9
 
6.7%
7 8
 
6.0%
6 8
 
6.0%
. 4
 
3.0%
Other values (2) 7
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
~ 25
18.7%
1 17
12.7%
8 16
11.9%
4 15
11.2%
2 15
11.2%
5 10
 
7.5%
0 9
 
6.7%
7 8
 
6.0%
6 8
 
6.0%
. 4
 
3.0%
Other values (2) 7
 
5.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
2022-12-15
52 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-15
2nd row2022-12-15
3rd row2022-12-15
4th row2022-12-15
5th row2022-12-15

Common Values

ValueCountFrequency (%)
2022-12-15 52
100.0%

Length

2023-12-12T19:06:03.055829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:06:03.164851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-15 52
100.0%

Interactions

2023-12-12T19:05:54.275895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:51.811733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:52.355999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:52.999193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:53.720612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:54.388288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:51.926339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:52.476802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:53.132586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:53.846623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:54.484417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:52.061818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:52.620199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:53.291951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:53.951262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:54.597912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:52.157920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:52.754489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:53.447912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:54.065109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:54.687176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:52.265252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:52.881382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:53.576539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:05:54.174227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:06:03.240192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동명도로명주소지번주소위도경도시설명코드시설종류지정일자CCTV설치대수보호구역도로폭
연번1.0000.9640.8810.8640.7090.9151.0001.0000.4050.5690.1990.779
동명0.9641.0000.9920.9920.8800.9891.0001.0000.0000.7540.6050.847
도로명주소0.8810.9921.0001.0001.0001.0000.9851.0000.0000.9860.9020.984
지번주소0.8640.9921.0001.0001.0001.0000.9841.0000.0000.9850.9020.984
위도0.7090.8801.0001.0001.0000.6090.9471.0000.0000.3230.0000.818
경도0.9150.9891.0001.0000.6091.0001.0001.0000.0000.0000.3130.777
시설명1.0001.0000.9850.9840.9471.0001.0001.0000.8661.0000.9200.990
코드1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시설종류0.4050.0000.0000.0000.0000.0000.8661.0001.0000.6980.4530.452
지정일자0.5690.7540.9860.9850.3230.0001.0001.0000.6981.0000.3010.889
CCTV설치대수0.1990.6050.9020.9020.0000.3130.9201.0000.4530.3011.0000.831
보호구역도로폭0.7790.8470.9840.9840.8180.7770.9901.0000.4520.8890.8311.000
2023-12-12T19:06:03.398547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관할경찰서명시설종류동명관리기관명
관할경찰서명1.0001.0001.0001.000
시설종류1.0001.0000.0001.000
동명1.0000.0001.0001.000
관리기관명1.0001.0001.0001.000
2023-12-12T19:06:03.524107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도지정일자CCTV설치대수동명시설종류관리기관명관할경찰서명
연번1.0000.1620.1400.086-0.2000.6920.2291.0001.000
위도0.1621.0000.1600.129-0.0730.4230.0001.0001.000
경도0.1400.1601.000-0.0870.1900.7110.0001.0001.000
지정일자0.0860.129-0.0871.000-0.6860.3260.5331.0001.000
CCTV설치대수-0.200-0.0730.190-0.6861.0000.2320.2841.0001.000
동명0.6920.4230.7110.3260.2321.0000.0001.0001.000
시설종류0.2290.0000.0000.5330.2840.0001.0001.0001.000
관리기관명1.0001.0001.0001.0001.0001.0001.0001.0001.000
관할경찰서명1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T19:05:54.828316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:05:55.091858image/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.
2023-12-12T19:05:55.267297image/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

연번시도자치구명동명도로명주소지번주소위도경도시설명코드시설종류지정일자관리기관명관할경찰서명CCTV설치여부CCTV설치대수보호구역도로폭데이터기준일자
01서울특별시성동구금호1가동서울특별시 성동구 행당로1길 13서울특별시 성동구 금호동1가 48-237.557465127.023576금북A초등학교1996서울특별시 성동구성동경찰서Y65~202022-12-15
12서울특별시성동구금호1가동서울특별시 성동구 난계로 18서울특별시 성동구 금호동1가 622-137.553736127.02489구립금일I어린이집2011서울특별시 성동구성동경찰서Y18~92022-12-15
23서울특별시성동구금호2.3가동서울특별시 성동구 무수막길 69서울특별시 성동구 금호동2가 51137.551965127.019743금호C초등학교1997서울특별시 성동구성동경찰서Y1062022-12-15
34서울특별시성동구금호3가동서울특별시 성동구 금호산2길 35서울특별시 성동구 금호동3가 165-137.548791127.023112금호유치원D유치원2009서울특별시 성동구성동경찰서Y142022-12-15
45서울특별시성동구금호4가동서울특별시 성동구 독서당로 251서울특별시 성동구 금호동4가 125437.546302127.017653금옥E초등학교1997서울특별시 성동구성동경찰서Y6302022-12-15
56서울특별시성동구금호4가동서울특별시 성동구 금호로1길 25서울특별시 성동구 금호동4가 440-1237.545529127.02187옥수F초등학교1997서울특별시 성동구성동경찰서Y36~62022-12-15
67서울특별시성동구금호4가동서울특별시 성동구 금호로1길 25서울특별시 성동구 금호동4가 440-1237.54553127.021871옥수병설G병설유치원2010서울특별시 성동구성동경찰서Y14~132022-12-15
78서울특별시성동구금호4가동서울특별시 성동구 독서당로 272서울특별시 성동구 금호동4가 80037.545329127.017941아이들세상유치원H유치원2010서울특별시 성동구성동경찰서Y15.52022-12-15
89서울특별시성동구금호동2가서울특별시 성동구 금호로 173. 신금호파크자이아파트 관리동서울특별시 성동구 금호동2가 137.556223127.018153신금호자이B어린이집2017서울특별시 성동구성동경찰서Y172022-12-15
910서울특별시성동구도선동서울특별시 성동구 무학로6길 9. 건강가정지원센터 1층<NA><NA><NA>왕도J어린이집2017<NA><NA><NA><NA><NA>2022-12-15
연번시도자치구명동명도로명주소지번주소위도경도시설명코드시설종류지정일자관리기관명관할경찰서명CCTV설치여부CCTV설치대수보호구역도로폭데이터기준일자
4243서울특별시성동구응봉동서울특별시 성동구 독서당로 434. 대림종합상가 3층서울특별시 성동구 응봉동 10037.553863127.034055세성유치원AR유치원2009서울특별시 성동구성동경찰서Y1242022-12-15
4344서울특별시성동구행당2동서울특별시 성동구 행당로 95서울특별시 성동구 행당동 317-1837.55771127.029358행현AS초등학교2005서울특별시 성동구성동경찰서Y312~202022-12-15
4445서울특별시성동구행당동서울특별시 성동구 고산자로8길 6서울특별시 성동구 행당동 320-6037.55702127.034872행당AT초등학교1996서울특별시 성동구성동경찰서Y54~82022-12-15
4546서울특별시성동구행당동서울특별시 성동구 금호로 100. 벽산아파트 유치원동서울특별시 성동구 금호동1가 63337.554532127.026093경동(벽산)유치원AU유치원2009서울특별시 성동구성동경찰서Y1282022-12-15
4647서울특별시성동구행당동서울특별시 성동구 고산자로2길 35서울특별시 성동구 행당동 34937.556483127.035274율화유치원AV유치원2006서울특별시 성동구성동경찰서Y1122022-12-15
4748서울특별시성동구행당동서울특별시 성동구 사근동10길 24서울특별시 성동구 사근동 14737.559476127.049075한양여대부속유치원AW유치원2010서울특별시 성동구성동경찰서Y172022-12-15
4849서울특별시성동구행당동서울특별시 성동구 독서당로63길 34서울특별시 성동구 행당동 341-4237.553126127.031303행응AX어린이집2006서울특별시 성동구성동경찰서Y172022-12-15
4950서울특별시성동구홍익동서울특별시 성동구 무학로10길 21서울특별시 성동구 홍익동 23637.566243127.033418성심유치원AY유치원2006서울특별시 성동구성동경찰서Y16~202022-12-15
5051서울특별시성동구홍익동서울특별시 성동구 무학로6길 41서울특별시 성동구 홍익동 15037.565129127.034625송곡유치원AZ유치원2009서울특별시 성동구성동경찰서Y2122022-12-15
5152서울특별시성동구홍익동서울특별시 성동구 마장로 141. 왕십리도선동주민센터 1층서울특별시 성동구 상왕십리동 81337.567832127.025529(구립)홍익K어린이집2016서울특별시 성동구성동경찰서Y172022-12-15