Overview

Dataset statistics

Number of variables10
Number of observations22
Missing cells64
Missing cells (%)29.1%
Duplicate rows1
Duplicate rows (%)4.5%
Total size in memory1.9 KiB
Average record size in memory90.0 B

Variable types

Text3
Numeric4
Categorical2
Boolean1

Dataset

Description서산시 관내 지역아동센터 등록현황으로 시설명칭, 대표자, 운영기관, 도로명 주소, 위치, 정원, 전화번호를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=445&beforeMenuCd=DOM_000000201001001000&publicdatapk=3070696

Alerts

운영여부 has constant value ""Constant
Dataset has 1 (4.5%) duplicate rowsDuplicates
운영주체 is highly overall correlated with 정원 and 4 other fieldsHigh correlation
데이터기준일 is highly overall correlated with 정원 and 4 other fieldsHigh correlation
정원 is highly overall correlated with 현원 and 2 other fieldsHigh correlation
현원 is highly overall correlated with 정원 and 2 other fieldsHigh correlation
경도(WGS84좌표) is highly overall correlated with 운영주체 and 1 other fieldsHigh correlation
위도(WGS84좌표) is highly overall correlated with 운영주체 and 1 other fieldsHigh correlation
상호 has 8 (36.4%) missing valuesMissing
도로명주소 has 8 (36.4%) missing valuesMissing
정원 has 8 (36.4%) missing valuesMissing
현원 has 8 (36.4%) missing valuesMissing
전화번호 has 8 (36.4%) missing valuesMissing
운영여부 has 8 (36.4%) missing valuesMissing
경도(WGS84좌표) has 8 (36.4%) missing valuesMissing
위도(WGS84좌표) has 8 (36.4%) missing valuesMissing

Reproduction

Analysis started2024-01-09 22:52:14.615217
Analysis finished2024-01-09 22:52:16.697556
Duration2.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상호
Text

MISSING 

Distinct14
Distinct (%)100.0%
Missing8
Missing (%)36.4%
Memory size308.0 B
2024-01-10T07:52:16.803469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.4285714
Min length3

Characters and Unicode

Total characters76
Distinct characters45
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

Unique14 ?
Unique (%)100.0%

Sample

1st row한아름공부방
2nd row미문공부방
3rd row산울공부방
4th row서정꿈나무교실
5th row은포공부방
ValueCountFrequency (%)
한아름공부방 1
 
7.1%
미문공부방 1
 
7.1%
산울공부방 1
 
7.1%
서정꿈나무교실 1
 
7.1%
은포공부방 1
 
7.1%
꿈이있는교실 1
 
7.1%
서해아이들 1
 
7.1%
행복한아이들 1
 
7.1%
중앙의샛별들 1
 
7.1%
방과후푸른교실 1
 
7.1%
Other values (4) 4
28.6%
2024-01-10T07:52:17.107769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
방 5
 
6.6%
한 4
 
5.3%
공 4
 
5.3%
부 4
 
5.3%
들 3
 
3.9%
아 3
 
3.9%
이 3
 
3.9%
실 3
 
3.9%
교 3
 
3.9%
꿈 3
 
3.9%
Other values (35) 41
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
방 5
 
6.6%
한 4
 
5.3%
공 4
 
5.3%
부 4
 
5.3%
들 3
 
3.9%
아 3
 
3.9%
이 3
 
3.9%
실 3
 
3.9%
교 3
 
3.9%
꿈 3
 
3.9%
Other values (35) 41
53.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
방 5
 
6.6%
한 4
 
5.3%
공 4
 
5.3%
부 4
 
5.3%
들 3
 
3.9%
아 3
 
3.9%
이 3
 
3.9%
실 3
 
3.9%
교 3
 
3.9%
꿈 3
 
3.9%
Other values (35) 41
53.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
방 5
 
6.6%
한 4
 
5.3%
공 4
 
5.3%
부 4
 
5.3%
들 3
 
3.9%
아 3
 
3.9%
이 3
 
3.9%
실 3
 
3.9%
교 3
 
3.9%
꿈 3
 
3.9%
Other values (35) 41
53.9%

도로명주소
Text

MISSING 

Distinct14
Distinct (%)100.0%
Missing8
Missing (%)36.4%
Memory size308.0 B
2024-01-10T07:52:17.280929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length27
Mean length26.142857
Min length17

Characters and Unicode

Total characters366
Distinct characters77
Distinct categories8 ?
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 (%)100.0%

Sample

1st row충청남도 서산시 음암면 도당가금말길 44-6
2nd row충청남도 서산시 음암면 상홍2길 98-1
3rd row충청남도 서산시 음암면 부산은골길 37
4th row충청남도 서산시 운산면 운암로 880, 201호(서정상가 2층)
5th row충청남도 서산시 고북면 고북1로 302-4
ValueCountFrequency (%)
충청남도 14
19.4%
서산시 14
19.4%
음암면 3
 
4.2%
덕지천로 2
 
2.8%
202호(청구제네스 1
 
1.4%
한마음8로 1
 
1.4%
41 1
 
1.4%
1(석남동 1
 
1.4%
해미면 1
 
1.4%
읍성마을3길 1
 
1.4%
Other values (33) 33
45.8%
2024-01-10T07:52:17.555048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57
 
15.6%
남 16
 
4.4%
산 16
 
4.4%
충 15
 
4.1%
도 15
 
4.1%
서 15
 
4.1%
청 15
 
4.1%
시 14
 
3.8%
1 12
 
3.3%
2 10
 
2.7%
Other values (67) 181
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 219
59.8%
Decimal Number 62
 
16.9%
Space Separator 57
 
15.6%
Open Punctuation 8
 
2.2%
Close Punctuation 8
 
2.2%
Dash Punctuation 6
 
1.6%
Other Punctuation 5
 
1.4%
Control 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 16
 
7.3%
산 16
 
7.3%
충 15
 
6.8%
도 15
 
6.8%
서 15
 
6.8%
청 15
 
6.8%
시 14
 
6.4%
동 10
 
4.6%
로 9
 
4.1%
면 8
 
3.7%
Other values (51) 86
39.3%
Decimal Number
ValueCountFrequency (%)
1 12
19.4%
2 10
16.1%
0 8
12.9%
3 6
9.7%
5 6
9.7%
4 5
8.1%
8 5
8.1%
7 5
8.1%
6 3
 
4.8%
9 2
 
3.2%
Space Separator
ValueCountFrequency (%)
57
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 219
59.8%
Common 147
40.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 16
 
7.3%
산 16
 
7.3%
충 15
 
6.8%
도 15
 
6.8%
서 15
 
6.8%
청 15
 
6.8%
시 14
 
6.4%
동 10
 
4.6%
로 9
 
4.1%
면 8
 
3.7%
Other values (51) 86
39.3%
Common
ValueCountFrequency (%)
57
38.8%
1 12
 
8.2%
2 10
 
6.8%
( 8
 
5.4%
0 8
 
5.4%
) 8
 
5.4%
3 6
 
4.1%
- 6
 
4.1%
5 6
 
4.1%
4 5
 
3.4%
Other values (6) 21
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 219
59.8%
ASCII 147
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57
38.8%
1 12
 
8.2%
2 10
 
6.8%
( 8
 
5.4%
0 8
 
5.4%
) 8
 
5.4%
3 6
 
4.1%
- 6
 
4.1%
5 6
 
4.1%
4 5
 
3.4%
Other values (6) 21
 
14.3%
Hangul
ValueCountFrequency (%)
남 16
 
7.3%
산 16
 
7.3%
충 15
 
6.8%
도 15
 
6.8%
서 15
 
6.8%
청 15
 
6.8%
시 14
 
6.4%
동 10
 
4.6%
로 9
 
4.1%
면 8
 
3.7%
Other values (51) 86
39.3%

정원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)57.1%
Missing8
Missing (%)36.4%
Infinite0
Infinite (%)0.0%
Mean31.5
Minimum19
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-01-10T07:52:17.654361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile19
Q120.75
median27
Q344.5
95-th percentile49
Maximum49
Range30
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation12.314782
Coefficient of variation (CV)0.39094545
Kurtosis-1.5507786
Mean31.5
Median Absolute Deviation (MAD)8
Skewness0.53656324
Sum441
Variance151.65385
MonotonicityNot monotonic
2024-01-10T07:52:17.734404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
49 3
 
13.6%
19 3
 
13.6%
29 2
 
9.1%
25 2
 
9.1%
40 1
 
4.5%
20 1
 
4.5%
46 1
 
4.5%
23 1
 
4.5%
(Missing) 8
36.4%
ValueCountFrequency (%)
19 3
13.6%
20 1
 
4.5%
23 1
 
4.5%
25 2
9.1%
29 2
9.1%
40 1
 
4.5%
46 1
 
4.5%
49 3
13.6%
ValueCountFrequency (%)
49 3
13.6%
46 1
 
4.5%
40 1
 
4.5%
29 2
9.1%
25 2
9.1%
23 1
 
4.5%
20 1
 
4.5%
19 3
13.6%

현원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)85.7%
Missing8
Missing (%)36.4%
Infinite0
Infinite (%)0.0%
Mean30.142857
Minimum15
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-01-10T07:52:17.824050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile16.3
Q120.75
median25
Q343.75
95-th percentile47.05
Maximum49
Range34
Interquartile range (IQR)23

Descriptive statistics

Standard deviation12.145636
Coefficient of variation (CV)0.4029358
Kurtosis-1.5329091
Mean30.142857
Median Absolute Deviation (MAD)7
Skewness0.45932048
Sum422
Variance147.51648
MonotonicityNot monotonic
2024-01-10T07:52:17.914214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
45 2
 
9.1%
25 2
 
9.1%
29 1
 
4.5%
49 1
 
4.5%
19 1
 
4.5%
24 1
 
4.5%
40 1
 
4.5%
46 1
 
4.5%
20 1
 
4.5%
23 1
 
4.5%
Other values (2) 2
 
9.1%
(Missing) 8
36.4%
ValueCountFrequency (%)
15 1
4.5%
17 1
4.5%
19 1
4.5%
20 1
4.5%
23 1
4.5%
24 1
4.5%
25 2
9.1%
29 1
4.5%
40 1
4.5%
45 2
9.1%
ValueCountFrequency (%)
49 1
4.5%
46 1
4.5%
45 2
9.1%
40 1
4.5%
29 1
4.5%
25 2
9.1%
24 1
4.5%
23 1
4.5%
20 1
4.5%
19 1
4.5%

운영주체
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
개인
14 
<NA>
8 

Length

Max length4
Median length2
Mean length2.7272727
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 14
63.6%
<NA> 8
36.4%

Length

2024-01-10T07:52:18.027702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:52:18.125583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 14
63.6%
na 8
36.4%

전화번호
Text

MISSING 

Distinct14
Distinct (%)100.0%
Missing8
Missing (%)36.4%
Memory size308.0 B
2024-01-10T07:52:18.254600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.071429
Min length12

Characters and Unicode

Total characters169
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)100.0%

Sample

1st row041-663-2151
2nd row041-664-8848
3rd row041-669-7736
4th row041-663-3765
5th row041-669-1993
ValueCountFrequency (%)
041-663-2151 1
 
7.1%
041-664-8848 1
 
7.1%
041-669-7736 1
 
7.1%
041-663-3765 1
 
7.1%
041-669-1993 1
 
7.1%
041-681-8011 1
 
7.1%
041-668-1005 1
 
7.1%
041-667-0191 1
 
7.1%
070-7106-0192 1
 
7.1%
041-668-2450 1
 
7.1%
Other values (4) 4
28.6%
2024-01-10T07:52:18.511116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 29
17.2%
- 28
16.6%
0 26
15.4%
1 25
14.8%
4 20
11.8%
8 10
 
5.9%
9 8
 
4.7%
3 7
 
4.1%
7 7
 
4.1%
2 5
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 141
83.4%
Dash Punctuation 28
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 29
20.6%
0 26
18.4%
1 25
17.7%
4 20
14.2%
8 10
 
7.1%
9 8
 
5.7%
3 7
 
5.0%
7 7
 
5.0%
2 5
 
3.5%
5 4
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 169
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 29
17.2%
- 28
16.6%
0 26
15.4%
1 25
14.8%
4 20
11.8%
8 10
 
5.9%
9 8
 
4.7%
3 7
 
4.1%
7 7
 
4.1%
2 5
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 169
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 29
17.2%
- 28
16.6%
0 26
15.4%
1 25
14.8%
4 20
11.8%
8 10
 
5.9%
9 8
 
4.7%
3 7
 
4.1%
7 7
 
4.1%
2 5
 
3.0%

운영여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)7.1%
Missing8
Missing (%)36.4%
Memory size176.0 B
True
14 
(Missing)
8 
ValueCountFrequency (%)
True 14
63.6%
(Missing) 8
36.4%
2024-01-10T07:52:18.856847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

경도(WGS84좌표)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)100.0%
Missing8
Missing (%)36.4%
Infinite0
Infinite (%)0.0%
Mean36.775206
Minimum36.667872
Maximum36.84281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-01-10T07:52:18.926045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.667872
5-th percentile36.695854
Q136.773196
median36.781286
Q336.792213
95-th percentile36.822845
Maximum36.84281
Range0.1749375
Interquartile range (IQR)0.019016175

Descriptive statistics

Standard deviation0.042753423
Coefficient of variation (CV)0.0011625611
Kurtosis2.5294202
Mean36.775206
Median Absolute Deviation (MAD)0.01019595
Skewness-1.268276
Sum514.85288
Variance0.0018278552
MonotonicityNot monotonic
2024-01-10T07:52:19.017431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
36.7888902 1
 
4.5%
36.7933201 1
 
4.5%
36.807416 1
 
4.5%
36.8120942 1
 
4.5%
36.6678723 1
 
4.5%
36.7776628 1
 
4.5%
36.7883249 1
 
4.5%
36.7849089 1
 
4.5%
36.7740012 1
 
4.5%
36.7751355 1
 
4.5%
Other values (4) 4
18.2%
(Missing) 8
36.4%
ValueCountFrequency (%)
36.6678723 1
4.5%
36.7109207 1
4.5%
36.7565999 1
4.5%
36.7729282 1
4.5%
36.7740012 1
4.5%
36.7751355 1
4.5%
36.7776628 1
4.5%
36.7849089 1
4.5%
36.7883249 1
4.5%
36.7888902 1
4.5%
ValueCountFrequency (%)
36.8428098 1
4.5%
36.8120942 1
4.5%
36.807416 1
4.5%
36.7933201 1
4.5%
36.7888902 1
4.5%
36.7883249 1
4.5%
36.7849089 1
4.5%
36.7776628 1
4.5%
36.7751355 1
4.5%
36.7740012 1
4.5%

위도(WGS84좌표)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)100.0%
Missing8
Missing (%)36.4%
Infinite0
Infinite (%)0.0%
Mean126.48137
Minimum126.42156
Maximum126.56585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-01-10T07:52:19.113404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.42156
5-th percentile126.43613
Q1126.45399
median126.46383
Q3126.5065
95-th percentile126.55242
Maximum126.56585
Range0.1442862
Interquartile range (IQR)0.05250565

Descriptive statistics

Standard deviation0.042782523
Coefficient of variation (CV)0.00033825159
Kurtosis-0.43940448
Mean126.48137
Median Absolute Deviation (MAD)0.0184874
Skewness0.75789501
Sum1770.7392
Variance0.0018303443
MonotonicityNot monotonic
2024-01-10T07:52:19.202881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
126.507839 1
 
4.5%
126.5024815 1
 
4.5%
126.4760936 1
 
4.5%
126.5658481 1
 
4.5%
126.5331949 1
 
4.5%
126.4439711 1
 
4.5%
126.4616849 1
 
4.5%
126.4659695 1
 
4.5%
126.4600416 1
 
4.5%
126.4537041 1
 
4.5%
Other values (4) 4
18.2%
(Missing) 8
36.4%
ValueCountFrequency (%)
126.4215619 1
4.5%
126.4439711 1
4.5%
126.4467085 1
4.5%
126.4537041 1
4.5%
126.4548636 1
4.5%
126.4600416 1
4.5%
126.4616849 1
4.5%
126.4659695 1
4.5%
126.4760936 1
4.5%
126.5024815 1
4.5%
ValueCountFrequency (%)
126.5658481 1
4.5%
126.5451923 1
4.5%
126.5331949 1
4.5%
126.507839 1
4.5%
126.5024815 1
4.5%
126.4760936 1
4.5%
126.4659695 1
4.5%
126.4616849 1
4.5%
126.4600416 1
4.5%
126.4548636 1
4.5%

데이터기준일
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
2016-03-21
14 
<NA>
8 

Length

Max length10
Median length10
Mean length7.8181818
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-03-21
2nd row2016-03-21
3rd row2016-03-21
4th row2016-03-21
5th row2016-03-21

Common Values

ValueCountFrequency (%)
2016-03-21 14
63.6%
<NA> 8
36.4%

Length

2024-01-10T07:52:19.317370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:52:19.408442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016-03-21 14
63.6%
na 8
36.4%

Interactions

2024-01-10T07:52:15.862956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:52:14.931614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:52:15.226189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:52:15.548216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:52:15.953629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:52:15.003478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:52:15.306360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:52:15.621201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:52:16.057863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:52:15.073049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:52:15.384347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:52:15.697780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:52:16.154437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:52:15.150599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:52:15.466785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:52:15.776323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:52:19.474964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상호도로명주소정원현원전화번호경도(WGS84좌표)위도(WGS84좌표)
상호1.0001.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.0001.000
정원1.0001.0001.0000.9181.0000.8160.521
현원1.0001.0000.9181.0001.0000.0000.490
전화번호1.0001.0001.0001.0001.0001.0001.000
경도(WGS84좌표)1.0001.0000.8160.0001.0001.0000.906
위도(WGS84좌표)1.0001.0000.5210.4901.0000.9061.000
2024-01-10T07:52:19.574274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운영주체데이터기준일
운영주체1.0001.000
데이터기준일1.0001.000
2024-01-10T07:52:19.648640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정원현원경도(WGS84좌표)위도(WGS84좌표)운영주체데이터기준일
정원1.0000.9690.1240.1841.0001.000
현원0.9691.0000.0880.1871.0001.000
경도(WGS84좌표)0.1240.0881.0000.0901.0001.000
위도(WGS84좌표)0.1840.1870.0901.0001.0001.000
운영주체1.0001.0001.0001.0001.0001.000
데이터기준일1.0001.0001.0001.0001.0001.000

Missing values

2024-01-10T07:52:16.287652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:52:16.448493image/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.
2024-01-10T07:52:16.594499image/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

상호도로명주소정원현원운영주체전화번호운영여부경도(WGS84좌표)위도(WGS84좌표)데이터기준일
0한아름공부방충청남도 서산시 음암면 도당가금말길 44-62929개인041-663-2151Y36.78889126.5078392016-03-21
1미문공부방충청남도 서산시 음암면 상홍2길 98-14949개인041-664-8848Y36.79332126.5024822016-03-21
2산울공부방충청남도 서산시 음암면 부산은골길 371919개인041-669-7736Y36.807416126.4760942016-03-21
3서정꿈나무교실충청남도 서산시 운산면 운암로 880, 201호(서정상가 2층)2924개인041-663-3765Y36.812094126.5658482016-03-21
4은포공부방충청남도 서산시 고북면 고북1로 302-44040개인041-669-1993Y36.667872126.5331952016-03-21
5꿈이있는교실충청남도 서산시 안견로 355(읍내동)4946개인041-681-8011Y36.777663126.4439712016-03-21
6서해아이들충청남도 서산시 학동1길 17(동문동)4945개인041-668-1005Y36.788325126.4616852016-03-21
7행복한아이들충청남도 서산시 고운로 275-6, 402호(동문동)2525개인041-667-0191Y36.784909126.4659692016-03-21
8중앙의샛별들충청남도 서산시 한마음8로 412020개인070-7106-0192Y36.774001126.4600422016-03-21
9방과후푸른교실충청남도 서산시 덕지천로 1(석남동)4645개인041-668-2450Y36.775135126.4537042016-03-21
상호도로명주소정원현원운영주체전화번호운영여부경도(WGS84좌표)위도(WGS84좌표)데이터기준일
12무지개돌봄충청남도 서산시 덕지천로 30, 2층(석남동)1917개인041-669-4147Y36.772928126.4548642016-03-21
13화목한충청남도 서산시 인지면 둔당로 105-9, 202호(청구제네스 제상가동)1915개인041-669-6208Y36.7566126.4215622016-03-21
14<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
15<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
16<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
17<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
18<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
19<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

상호도로명주소정원현원운영주체전화번호운영여부경도(WGS84좌표)위도(WGS84좌표)데이터기준일# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>8