Overview

Dataset statistics

Number of variables19
Number of observations1895
Missing cells2065
Missing cells (%)5.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory294.4 KiB
Average record size in memory159.1 B

Variable types

Numeric5
Unsupported1
Text6
Categorical7

Dataset

Description전국 국립공원 주변의 문화시설에 대한 상세한 시설명 및 위치(위/경도)등 융합 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2991

Alerts

수정일자 has constant value ""Constant
관광지리명 is highly overall correlated with 시설경도 and 9 other fieldsHigh correlation
시군구명 is highly overall correlated with 시설경도 and 9 other fieldsHigh correlation
법정동명 is highly overall correlated with 시설경도 and 9 other fieldsHigh correlation
국립공원지번주소 is highly overall correlated with 시설경도 and 9 other fieldsHigh correlation
시도명 is highly overall correlated with 시설경도 and 9 other fieldsHigh correlation
국립공원명 is highly overall correlated with 시설경도 and 9 other fieldsHigh correlation
시설경도 is highly overall correlated with 문화시설경도 and 8 other fieldsHigh correlation
문화시설경도 is highly overall correlated with 시설경도 and 6 other fieldsHigh correlation
시설위도 is highly overall correlated with 시설경도 and 7 other fieldsHigh correlation
POI_ID is highly overall correlated with 국립공원명 and 5 other fieldsHigh correlation
문화시설위도 is highly overall correlated with 시설경도 and 7 other fieldsHigh correlation
해발높이수 has 1895 (100.0%) missing valuesMissing
건물번호 has 82 (4.3%) missing valuesMissing
도로명 has 82 (4.3%) missing valuesMissing
고유ID has unique valuesUnique
해발높이수 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-01-09 20:16:33.801036
Analysis finished2024-01-09 20:16:38.219157
Duration4.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설경도
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.5336
Minimum125.35154
Maximum129.21547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.8 KiB
2024-01-10T05:16:38.273361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.35154
5-th percentile126.23217
Q1126.88906
median127.73035
Q3128.05525
95-th percentile129.21547
Maximum129.21547
Range3.8639317
Interquartile range (IQR)1.1661914

Descriptive statistics

Standard deviation0.89459152
Coefficient of variation (CV)0.0070145555
Kurtosis-0.53378036
Mean127.5336
Median Absolute Deviation (MAD)0.7210664
Skewness0.083572759
Sum241676.17
Variance0.80029399
MonotonicityNot monotonic
2024-01-10T05:16:38.392228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
126.5291169 279
 
14.7%
129.2154719 148
 
7.8%
128.0552509 124
 
6.5%
127.0092819 112
 
5.9%
127.7921787 103
 
5.4%
127.7738435 103
 
5.4%
127.4761673 103
 
5.4%
126.985927 88
 
4.6%
127.2085434 73
 
3.9%
128.1061875 56
 
3.0%
Other values (24) 706
37.3%
ValueCountFrequency (%)
125.3515402 27
 
1.4%
125.9092585 27
 
1.4%
125.9788701 32
 
1.7%
126.2321743 21
 
1.1%
126.5291169 279
14.7%
126.6003454 26
 
1.4%
126.7038479 30
 
1.6%
126.7414123 27
 
1.4%
126.8890595 48
 
2.5%
126.985927 88
 
4.6%
ValueCountFrequency (%)
129.2154719 148
7.8%
129.1602149 19
 
1.0%
128.9160572 16
 
0.8%
128.6871146 49
 
2.6%
128.542052 23
 
1.2%
128.4852256 31
 
1.6%
128.4561965 49
 
2.6%
128.4217744 26
 
1.4%
128.120319 23
 
1.2%
128.1061875 56
 
3.0%

해발높이수
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1895
Missing (%)100.0%
Memory size16.8 KiB

건물번호
Text

MISSING 

Distinct780
Distinct (%)43.0%
Missing82
Missing (%)4.3%
Memory size14.9 KiB
2024-01-10T05:16:38.689499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.2901269
Min length1

Characters and Unicode

Total characters5965
Distinct characters14
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

Unique467 ?
Unique (%)25.8%

Sample

1st row15
2nd row377
3rd row26268
4th row17-36
5th row186
ValueCountFrequency (%)
1 38
 
2.0%
9 26
 
1.4%
15 20
 
1.0%
36 19
 
1.0%
32 18
 
0.9%
01일 17
 
0.9%
12 17
 
0.9%
33 16
 
0.8%
01월 16
 
0.8%
18 15
 
0.8%
Other values (734) 1713
89.5%
2024-01-10T05:16:39.104659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1036
17.4%
2 700
11.7%
3 581
9.7%
5 508
8.5%
0 500
8.4%
4 486
8.1%
6 431
7.2%
7 398
 
6.7%
8 368
 
6.2%
9 360
 
6.0%
Other values (4) 597
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5368
90.0%
Dash Punctuation 291
 
4.9%
Other Letter 204
 
3.4%
Space Separator 102
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1036
19.3%
2 700
13.0%
3 581
10.8%
5 508
9.5%
0 500
9.3%
4 486
9.1%
6 431
8.0%
7 398
 
7.4%
8 368
 
6.9%
9 360
 
6.7%
Other Letter
ValueCountFrequency (%)
월 102
50.0%
일 102
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 291
100.0%
Space Separator
ValueCountFrequency (%)
102
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5761
96.6%
Hangul 204
 
3.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1036
18.0%
2 700
12.2%
3 581
10.1%
5 508
8.8%
0 500
8.7%
4 486
8.4%
6 431
7.5%
7 398
 
6.9%
8 368
 
6.4%
9 360
 
6.2%
Other values (2) 393
 
6.8%
Hangul
ValueCountFrequency (%)
월 102
50.0%
일 102
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5761
96.6%
Hangul 204
 
3.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1036
18.0%
2 700
12.2%
3 581
10.1%
5 508
8.8%
0 500
8.7%
4 486
8.4%
6 431
7.5%
7 398
 
6.9%
8 368
 
6.4%
9 360
 
6.2%
Other values (2) 393
 
6.8%
Hangul
ValueCountFrequency (%)
월 102
50.0%
일 102
50.0%

문화시설경도
Real number (ℝ)

HIGH CORRELATION 

Distinct1586
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.52245
Minimum125.42762
Maximum129.51136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.8 KiB
2024-01-10T05:16:39.235355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.42762
5-th percentile126.26644
Q1126.83266
median127.67526
Q3128.04221
95-th percentile129.21381
Maximum129.51136
Range4.0837436
Interquartile range (IQR)1.2095508

Descriptive statistics

Standard deviation0.87017605
Coefficient of variation (CV)0.0068237084
Kurtosis-0.68621545
Mean127.52245
Median Absolute Deviation (MAD)0.7476606
Skewness0.29342854
Sum241655.05
Variance0.75720635
MonotonicityNot monotonic
2024-01-10T05:16:39.354596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.357352 4
 
0.2%
127.7214924 3
 
0.2%
127.7279615 3
 
0.2%
127.7251562 3
 
0.2%
127.7268808 3
 
0.2%
127.7070169 3
 
0.2%
127.7353983 3
 
0.2%
127.6568189 3
 
0.2%
127.6671378 3
 
0.2%
127.6601562 3
 
0.2%
Other values (1576) 1864
98.4%
ValueCountFrequency (%)
125.4276183 2
0.1%
125.4300973 2
0.1%
125.9275324 2
0.1%
125.9464322 2
0.1%
125.9964804 2
0.1%
125.9968772 2
0.1%
126.0283913 2
0.1%
126.0317445 2
0.1%
126.0377287 2
0.1%
126.0614313 2
0.1%
ValueCountFrequency (%)
129.5113619 1
0.1%
129.4965864 1
0.1%
129.4827634 1
0.1%
129.4699871 1
0.1%
129.4346041 1
0.1%
129.3853634 1
0.1%
129.3340193 1
0.1%
129.3333228 1
0.1%
129.3319927 1
0.1%
129.3287574 1
0.1%

문화시설명
Text

Distinct1529
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
2024-01-10T05:16:39.548810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length6.6496042
Min length1

Characters and Unicode

Total characters12601
Distinct characters597
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

Unique1322 ?
Unique (%)69.8%

Sample

1st row교보서적
2nd row교보서점
3rd row구산문화관
4th row구토란요
5th row국립경주박물관
ValueCountFrequency (%)
cgv 12
 
0.6%
메가박스 10
 
0.5%
제일서점 7
 
0.4%
이마트문화센터 7
 
0.4%
동아서점 7
 
0.4%
제일서적 6
 
0.3%
롯데시네마 6
 
0.3%
현대서점 5
 
0.3%
대양서림 5
 
0.3%
문화서점 5
 
0.3%
Other values (1519) 1825
96.3%
2024-01-10T05:16:39.848277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
관 744
 
5.9%
서 397
 
3.2%
문 293
 
2.3%
점 252
 
2.0%
전 237
 
1.9%
박 229
 
1.8%
화 227
 
1.8%
시 218
 
1.7%
물 195
 
1.5%
수 184
 
1.5%
Other values (587) 9625
76.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12237
97.1%
Decimal Number 190
 
1.5%
Uppercase Letter 131
 
1.0%
Close Punctuation 19
 
0.2%
Open Punctuation 19
 
0.2%
Other Punctuation 4
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 744
 
6.1%
서 397
 
3.2%
문 293
 
2.4%
점 252
 
2.1%
전 237
 
1.9%
박 229
 
1.9%
화 227
 
1.9%
시 218
 
1.8%
물 195
 
1.6%
수 184
 
1.5%
Other values (550) 9261
75.7%
Uppercase Letter
ValueCountFrequency (%)
G 17
13.0%
C 14
10.7%
E 14
10.7%
V 14
10.7%
A 10
 
7.6%
B 8
 
6.1%
I 8
 
6.1%
N 6
 
4.6%
O 6
 
4.6%
T 5
 
3.8%
Other values (14) 29
22.1%
Decimal Number
ValueCountFrequency (%)
2 81
42.6%
0 43
22.6%
1 31
 
16.3%
3 16
 
8.4%
4 10
 
5.3%
6 4
 
2.1%
5 3
 
1.6%
7 1
 
0.5%
8 1
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12237
97.1%
Common 233
 
1.8%
Latin 131
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 744
 
6.1%
서 397
 
3.2%
문 293
 
2.4%
점 252
 
2.1%
전 237
 
1.9%
박 229
 
1.9%
화 227
 
1.9%
시 218
 
1.8%
물 195
 
1.6%
수 184
 
1.5%
Other values (550) 9261
75.7%
Latin
ValueCountFrequency (%)
G 17
13.0%
C 14
10.7%
E 14
10.7%
V 14
10.7%
A 10
 
7.6%
B 8
 
6.1%
I 8
 
6.1%
N 6
 
4.6%
O 6
 
4.6%
T 5
 
3.8%
Other values (14) 29
22.1%
Common
ValueCountFrequency (%)
2 81
34.8%
0 43
18.5%
1 31
 
13.3%
) 19
 
8.2%
( 19
 
8.2%
3 16
 
6.9%
4 10
 
4.3%
. 4
 
1.7%
6 4
 
1.7%
5 3
 
1.3%
Other values (3) 3
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12237
97.1%
ASCII 364
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
관 744
 
6.1%
서 397
 
3.2%
문 293
 
2.4%
점 252
 
2.1%
전 237
 
1.9%
박 229
 
1.9%
화 227
 
1.9%
시 218
 
1.8%
물 195
 
1.6%
수 184
 
1.5%
Other values (550) 9261
75.7%
ASCII
ValueCountFrequency (%)
2 81
22.3%
0 43
11.8%
1 31
 
8.5%
) 19
 
5.2%
( 19
 
5.2%
G 17
 
4.7%
3 16
 
4.4%
C 14
 
3.8%
E 14
 
3.8%
V 14
 
3.8%
Other values (27) 96
26.4%

시설위도
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.293119
Minimum33.362552
Maximum38.13681
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.8 KiB
2024-01-10T05:16:39.978527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.362552
5-th percentile33.362552
Q134.529216
median34.944647
Q336.372187
95-th percentile37.679325
Maximum38.13681
Range4.7742574
Interquartile range (IQR)1.8429709

Descriptive statistics

Standard deviation1.3370311
Coefficient of variation (CV)0.03788362
Kurtosis-0.83502468
Mean35.293119
Median Absolute Deviation (MAD)0.85642687
Skewness0.35469337
Sum66880.461
Variance1.7876522
MonotonicityNot monotonic
2024-01-10T05:16:40.096824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
33.36255233 279
 
14.7%
35.80107418 148
 
7.8%
37.36534564 124
 
6.5%
35.12448894 112
 
5.9%
34.52921604 103
 
5.4%
34.76708742 103
 
5.4%
34.05831603 103
 
5.4%
37.67932521 88
 
4.6%
36.37218689 73
 
3.9%
36.8864867 56
 
3.0%
Other values (24) 706
37.3%
ValueCountFrequency (%)
33.36255233 279
14.7%
34.05831603 103
 
5.4%
34.21739833 27
 
1.4%
34.29966274 32
 
1.7%
34.46441367 27
 
1.4%
34.52921604 103
 
5.4%
34.62802952 27
 
1.4%
34.64868568 27
 
1.4%
34.69920484 27
 
1.4%
34.75668141 49
 
2.6%
ValueCountFrequency (%)
38.1368097 26
 
1.4%
37.79833557 23
 
1.2%
37.67932521 88
4.6%
37.36534564 124
6.5%
37.09890554 16
 
0.8%
36.96035421 31
 
1.6%
36.8864867 56
3.0%
36.67289793 21
 
1.1%
36.58601916 33
 
1.7%
36.38835142 19
 
1.0%

수정일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
20230207
1895 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20230207
2nd row20230207
3rd row20230207
4th row20230207
5th row20230207

Common Values

ValueCountFrequency (%)
20230207 1895
100.0%

Length

2024-01-10T05:16:40.218469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:16:40.307400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20230207 1895
100.0%

POI_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7448340.7
Minimum296556
Maximum17847405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.8 KiB
2024-01-10T05:16:40.410736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum296556
5-th percentile546255
Q1546273
median546282
Q317847086
95-th percentile17847405
Maximum17847405
Range17550849
Interquartile range (IQR)17300813

Descriptive statistics

Standard deviation7811095.3
Coefficient of variation (CV)1.0487027
Kurtosis-1.6729966
Mean7448340.7
Median Absolute Deviation (MAD)249726
Skewness0.40587781
Sum1.4114606 × 1010
Variance6.101321 × 1013
MonotonicityNot monotonic
2024-01-10T05:16:40.543346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
546273 279
 
14.7%
11388603 148
 
7.8%
546260 124
 
6.5%
546255 112
 
5.9%
17847042 103
 
5.4%
17847405 103
 
5.4%
17847103 103
 
5.4%
6467203 88
 
4.6%
546279 73
 
3.9%
546278 56
 
3.0%
Other values (24) 706
37.3%
ValueCountFrequency (%)
296556 33
 
1.7%
546255 112
5.9%
546260 124
6.5%
546261 23
 
1.2%
546262 19
 
1.0%
546266 21
 
1.1%
546267 30
 
1.6%
546271 26
 
1.4%
546273 279
14.7%
546275 24
 
1.3%
ValueCountFrequency (%)
17847405 103
5.4%
17847400 33
 
1.7%
17847395 30
 
1.6%
17847277 32
 
1.7%
17847142 27
 
1.4%
17847131 27
 
1.4%
17847114 49
2.6%
17847109 49
2.6%
17847103 103
5.4%
17847086 27
 
1.4%

고유ID
Text

UNIQUE 

Distinct1895
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
2024-01-10T05:16:40.733419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters36005
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1895 ?
Unique (%)100.0%

Sample

1st rowKCNCCPO22N000000058
2nd rowKCNCCPO22N000000059
3rd rowKCNCCPO22N000000060
4th rowKCNCCPO22N000000061
5th rowKCNCCPO22N000000062
ValueCountFrequency (%)
kcnccpo22n000000058 1
 
0.1%
kcnccpo22n000000899 1
 
0.1%
kcnccpo22n000001325 1
 
0.1%
kcnccpo22n000001324 1
 
0.1%
kcnccpo22n000001323 1
 
0.1%
kcnccpo22n000001322 1
 
0.1%
kcnccpo22n000001321 1
 
0.1%
kcnccpo22n000001320 1
 
0.1%
kcnccpo22n000001319 1
 
0.1%
kcnccpo22n000000875 1
 
0.1%
Other values (1885) 1885
99.5%
2024-01-10T05:16:41.019715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11051
30.7%
C 5685
15.8%
2 4370
 
12.1%
N 3790
 
10.5%
K 1895
 
5.3%
P 1895
 
5.3%
O 1895
 
5.3%
1 1476
 
4.1%
5 580
 
1.6%
3 580
 
1.6%
Other values (5) 2788
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20845
57.9%
Uppercase Letter 15160
42.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11051
53.0%
2 4370
 
21.0%
1 1476
 
7.1%
5 580
 
2.8%
3 580
 
2.8%
4 580
 
2.8%
7 579
 
2.8%
6 579
 
2.8%
8 575
 
2.8%
9 475
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
C 5685
37.5%
N 3790
25.0%
K 1895
 
12.5%
P 1895
 
12.5%
O 1895
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common 20845
57.9%
Latin 15160
42.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11051
53.0%
2 4370
 
21.0%
1 1476
 
7.1%
5 580
 
2.8%
3 580
 
2.8%
4 580
 
2.8%
7 579
 
2.8%
6 579
 
2.8%
8 575
 
2.8%
9 475
 
2.3%
Latin
ValueCountFrequency (%)
C 5685
37.5%
N 3790
25.0%
K 1895
 
12.5%
P 1895
 
12.5%
O 1895
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11051
30.7%
C 5685
15.8%
2 4370
 
12.1%
N 3790
 
10.5%
K 1895
 
5.3%
P 1895
 
5.3%
O 1895
 
5.3%
1 1476
 
4.1%
5 580
 
1.6%
3 580
 
1.6%
Other values (5) 2788
 
7.7%

번지번호
Text

Distinct1293
Distinct (%)68.4%
Missing6
Missing (%)0.3%
Memory size14.9 KiB
2024-01-10T05:16:41.322536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.6670196
Min length1

Characters and Unicode

Total characters8816
Distinct characters15
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

Unique940 ?
Unique (%)49.8%

Sample

1st row521-27
2nd row715-19
3rd row164-1
4th row1166
5th row76
ValueCountFrequency (%)
2005 15
 
0.8%
01월 14
 
0.7%
03월 12
 
0.6%
445 9
 
0.5%
44927 9
 
0.5%
130 8
 
0.4%
914 8
 
0.4%
04월 8
 
0.4%
588 7
 
0.4%
774-1 6
 
0.3%
Other values (1277) 1851
95.1%
2024-01-10T05:16:41.921697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1543
17.5%
- 1008
11.4%
2 949
10.8%
3 786
8.9%
4 703
8.0%
0 681
7.7%
5 661
7.5%
6 618
7.0%
7 588
 
6.7%
9 540
 
6.1%
Other values (5) 739
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7583
86.0%
Dash Punctuation 1008
 
11.4%
Other Letter 167
 
1.9%
Space Separator 58
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1543
20.3%
2 949
12.5%
3 786
10.4%
4 703
9.3%
0 681
9.0%
5 661
8.7%
6 618
8.1%
7 588
 
7.8%
9 540
 
7.1%
8 514
 
6.8%
Other Letter
ValueCountFrequency (%)
월 58
34.7%
일 58
34.7%
산 51
30.5%
Dash Punctuation
ValueCountFrequency (%)
- 1008
100.0%
Space Separator
ValueCountFrequency (%)
58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8649
98.1%
Hangul 167
 
1.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1543
17.8%
- 1008
11.7%
2 949
11.0%
3 786
9.1%
4 703
8.1%
0 681
7.9%
5 661
7.6%
6 618
7.1%
7 588
 
6.8%
9 540
 
6.2%
Other values (2) 572
 
6.6%
Hangul
ValueCountFrequency (%)
월 58
34.7%
일 58
34.7%
산 51
30.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8649
98.1%
Hangul 167
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1543
17.8%
- 1008
11.7%
2 949
11.0%
3 786
9.1%
4 703
8.1%
0 681
7.9%
5 661
7.6%
6 618
7.1%
7 588
 
6.8%
9 540
 
6.2%
Other values (2) 572
 
6.6%
Hangul
ValueCountFrequency (%)
월 58
34.7%
일 58
34.7%
산 51
30.5%

문화시설위도
Real number (ℝ)

HIGH CORRELATION 

Distinct1586
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.384261
Minimum33.295296
Maximum38.265184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.8 KiB
2024-01-10T05:16:42.048418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.295296
5-th percentile33.473869
Q134.740395
median34.995006
Q336.415692
95-th percentile37.635268
Maximum38.265184
Range4.9698875
Interquartile range (IQR)1.6752964

Descriptive statistics

Standard deviation1.2769713
Coefficient of variation (CV)0.03608868
Kurtosis-0.77886946
Mean35.384261
Median Absolute Deviation (MAD)0.81159036
Skewness0.32835763
Sum67053.175
Variance1.6306557
MonotonicityNot monotonic
2024-01-10T05:16:42.162058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.34719153 4
 
0.2%
34.72833398 3
 
0.2%
34.74710217 3
 
0.2%
34.75457989 3
 
0.2%
34.7545362 3
 
0.2%
34.76215049 3
 
0.2%
34.75685149 3
 
0.2%
34.75797973 3
 
0.2%
34.7628917 3
 
0.2%
34.7613236 3
 
0.2%
Other values (1576) 1864
98.4%
ValueCountFrequency (%)
33.29529599 1
0.1%
33.30576967 1
0.1%
33.31474697 1
0.1%
33.31540972 1
0.1%
33.31589376 1
0.1%
33.32288158 1
0.1%
33.3246581 2
0.1%
33.32996354 1
0.1%
33.33569503 1
0.1%
33.33588481 1
0.1%
ValueCountFrequency (%)
38.26518352 1
0.1%
38.23123051 1
0.1%
38.22015012 1
0.1%
38.21336734 1
0.1%
38.18892001 1
0.1%
38.18865123 1
0.1%
38.18707376 1
0.1%
38.14544636 1
0.1%
38.12017183 1
0.1%
38.07372991 1
0.1%

국립공원명
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
다도해해상국립공원
373 
한려해상국립공원
297 
한라산국립공원
279 
경주국립공원
148 
치악산국립공원
124 
Other values (17)
674 

Length

Max length9
Median length7
Mean length7.4970976
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
다도해해상국립공원 373
19.7%
한려해상국립공원 297
15.7%
한라산국립공원 279
14.7%
경주국립공원 148
 
7.8%
치악산국립공원 124
 
6.5%
무등산국립공원 112
 
5.9%
북한산국립공원 88
 
4.6%
계룡산국립공원 73
 
3.9%
월악산국립공원 56
 
3.0%
내장산국립공원 48
 
2.5%
Other values (12) 297
15.7%

Length

2024-01-10T05:16:42.281608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
다도해해상국립공원 373
19.7%
한려해상국립공원 297
15.7%
한라산국립공원 279
14.7%
경주국립공원 148
 
7.8%
치악산국립공원 124
 
6.5%
무등산국립공원 112
 
5.9%
북한산국립공원 88
 
4.6%
계룡산국립공원 73
 
3.9%
월악산국립공원 56
 
3.0%
내장산국립공원 48
 
2.5%
Other values (12) 297
15.7%

관광지리명
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
<NA>
545 
광령리
279 
학곡리
124 
동도리
103 
유송리
103 
Other values (23)
741 

Length

Max length4
Median length3
Mean length3.3208443
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 545
28.8%
광령리 279
14.7%
학곡리 124
 
6.5%
동도리 103
 
5.4%
유송리 103
 
5.4%
상신리 73
 
3.9%
수산리 56
 
3.0%
갈곶리 49
 
2.6%
두억리 49
 
2.6%
중벌리 33
 
1.7%
Other values (18) 481
25.4%

Length

2024-01-10T05:16:42.400762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 545
28.8%
광령리 279
14.7%
학곡리 124
 
6.5%
동도리 103
 
5.4%
유송리 103
 
5.4%
상신리 73
 
3.9%
수산리 56
 
3.0%
갈곶리 49
 
2.6%
두억리 49
 
2.6%
중벌리 33
 
1.7%
Other values (18) 481
25.4%

문화시설격자코드
Text

Distinct1425
Distinct (%)75.2%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
2024-01-10T05:16:42.660905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters15160
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1122 ?
Unique (%)59.2%

Sample

1st row마마546644
2nd row마마541629
3rd row마마499672
4th row마마501519
5th row마마560605
ValueCountFrequency (%)
라라229399 9
 
0.5%
다라814242 8
 
0.4%
라라202371 6
 
0.3%
라라207400 6
 
0.3%
라라153405 6
 
0.3%
라라262235 6
 
0.3%
라라118495 6
 
0.3%
라라218385 6
 
0.3%
라라189408 6
 
0.3%
라라208400 6
 
0.3%
Other values (1415) 1830
96.6%
2024-01-10T05:16:43.016952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
라 1605
10.6%
4 1357
9.0%
2 1314
8.7%
0 1223
8.1%
5 1189
 
7.8%
3 1171
 
7.7%
6 1093
 
7.2%
1 1087
 
7.2%
9 1085
 
7.2%
8 1060
 
7.0%
Other values (7) 2976
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11370
75.0%
Other Letter 3790
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1357
11.9%
2 1314
11.6%
0 1223
10.8%
5 1189
10.5%
3 1171
10.3%
6 1093
9.6%
1 1087
9.6%
9 1085
9.5%
8 1060
9.3%
7 791
7.0%
Other Letter
ValueCountFrequency (%)
라 1605
42.3%
다 867
22.9%
마 511
 
13.5%
사 298
 
7.9%
나 297
 
7.8%
바 191
 
5.0%
아 21
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 11370
75.0%
Hangul 3790
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1357
11.9%
2 1314
11.6%
0 1223
10.8%
5 1189
10.5%
3 1171
10.3%
6 1093
9.6%
1 1087
9.6%
9 1085
9.5%
8 1060
9.3%
7 791
7.0%
Hangul
ValueCountFrequency (%)
라 1605
42.3%
다 867
22.9%
마 511
 
13.5%
사 298
 
7.9%
나 297
 
7.8%
바 191
 
5.0%
아 21
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11370
75.0%
Hangul 3790
 
25.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
라 1605
42.3%
다 867
22.9%
마 511
 
13.5%
사 298
 
7.9%
나 297
 
7.8%
바 191
 
5.0%
아 21
 
0.6%
ASCII
ValueCountFrequency (%)
4 1357
11.9%
2 1314
11.6%
0 1223
10.8%
5 1189
10.5%
3 1171
10.3%
6 1093
9.6%
1 1087
9.6%
9 1085
9.5%
8 1060
9.3%
7 791
7.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
여수시
309 
제주시
279 
경주시
148 
원주시
124 
북구
112 
Other values (24)
923 

Length

Max length7
Median length3
Mean length3.1266491
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여수시 309
16.3%
제주시 279
14.7%
경주시 148
 
7.8%
원주시 124
 
6.5%
북구 112
 
5.9%
고양시 덕양구 88
 
4.6%
공주시 73
 
3.9%
남해군 66
 
3.5%
제천시 56
 
3.0%
고흥군 54
 
2.8%
Other values (19) 586
30.9%

Length

2024-01-10T05:16:43.141704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
여수시 309
15.6%
제주시 279
14.1%
경주시 148
 
7.5%
원주시 124
 
6.3%
북구 112
 
5.6%
고양시 88
 
4.4%
덕양구 88
 
4.4%
공주시 73
 
3.7%
남해군 66
 
3.3%
제천시 56
 
2.8%
Other values (20) 640
32.3%

국립공원지번주소
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
제주특별자치도 제주시 애월읍 광령리 산183-6
279 
경상북도 경주시 배동 산49
148 
강원도 원주시 소초면 학곡리 산33
124 
광주광역시 북구 금곡동 산1-1
 
112
전라남도 여수시 남면 유송리
 
103
Other values (29)
1129 

Length

Max length26
Median length21
Mean length19.185224
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도 경주시 배동 산49
2nd row경상북도 경주시 배동 산49
3rd row경상북도 경주시 배동 산49
4th row경상북도 경주시 배동 산49
5th row경상북도 경주시 배동 산49

Common Values

ValueCountFrequency (%)
제주특별자치도 제주시 애월읍 광령리 산183-6 279
 
14.7%
경상북도 경주시 배동 산49 148
 
7.8%
강원도 원주시 소초면 학곡리 산33 124
 
6.5%
광주광역시 북구 금곡동 산1-1 112
 
5.9%
전라남도 여수시 남면 유송리 103
 
5.4%
전라남도 여수시 만흥동 103
 
5.4%
전라남도 여수시 삼산면 동도리 103
 
5.4%
경기도 고양시 덕양구 효자동 산1-1 88
 
4.6%
충청남도 공주시 반포면 상신리 산11-2 73
 
3.9%
충청북도 제천시 덕산면 수산리 산44-2 56
 
3.0%
Other values (24) 706
37.3%

Length

2024-01-10T05:16:43.244900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전라남도 506
 
6.0%
여수시 309
 
3.7%
제주특별자치도 279
 
3.3%
애월읍 279
 
3.3%
광령리 279
 
3.3%
산183-6 279
 
3.3%
제주시 279
 
3.3%
경상남도 241
 
2.9%
산1-1 223
 
2.6%
경상북도 200
 
2.4%
Other values (110) 5572
66.0%

도로명
Text

MISSING 

Distinct925
Distinct (%)51.0%
Missing82
Missing (%)4.3%
Memory size14.9 KiB
2024-01-10T05:16:43.561024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.0849421
Min length3

Characters and Unicode

Total characters7406
Distinct characters322
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique556 ?
Unique (%)30.7%

Sample

1st row황성로1번길
2nd row금성로
3rd row구산서원길
4th row귀계길
5th row일정로
ValueCountFrequency (%)
중앙로 42
 
2.3%
박람회길 21
 
1.2%
오동도로 15
 
0.8%
좌수영로 15
 
0.8%
동문로 12
 
0.7%
보문로 12
 
0.7%
고흥로 12
 
0.7%
서원대로 11
 
0.6%
시청로 10
 
0.6%
남해대로 10
 
0.6%
Other values (915) 1653
91.2%
2024-01-10T05:16:44.029589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 1252
 
16.9%
길 742
 
10.0%
1 212
 
2.9%
대 143
 
1.9%
동 138
 
1.9%
산 123
 
1.7%
번 120
 
1.6%
서 110
 
1.5%
문 96
 
1.3%
원 94
 
1.3%
Other values (312) 4376
59.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6703
90.5%
Decimal Number 703
 
9.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 1252
 
18.7%
길 742
 
11.1%
대 143
 
2.1%
동 138
 
2.1%
산 123
 
1.8%
번 120
 
1.8%
서 110
 
1.6%
문 96
 
1.4%
원 94
 
1.4%
남 93
 
1.4%
Other values (302) 3792
56.6%
Decimal Number
ValueCountFrequency (%)
1 212
30.2%
2 92
13.1%
3 69
 
9.8%
7 62
 
8.8%
5 61
 
8.7%
4 56
 
8.0%
0 52
 
7.4%
6 43
 
6.1%
9 29
 
4.1%
8 27
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6703
90.5%
Common 703
 
9.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 1252
 
18.7%
길 742
 
11.1%
대 143
 
2.1%
동 138
 
2.1%
산 123
 
1.8%
번 120
 
1.8%
서 110
 
1.6%
문 96
 
1.4%
원 94
 
1.4%
남 93
 
1.4%
Other values (302) 3792
56.6%
Common
ValueCountFrequency (%)
1 212
30.2%
2 92
13.1%
3 69
 
9.8%
7 62
 
8.8%
5 61
 
8.7%
4 56
 
8.0%
0 52
 
7.4%
6 43
 
6.1%
9 29
 
4.1%
8 27
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6703
90.5%
ASCII 703
 
9.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 1252
 
18.7%
길 742
 
11.1%
대 143
 
2.1%
동 138
 
2.1%
산 123
 
1.8%
번 120
 
1.8%
서 110
 
1.6%
문 96
 
1.4%
원 94
 
1.4%
남 93
 
1.4%
Other values (302) 3792
56.6%
ASCII
ValueCountFrequency (%)
1 212
30.2%
2 92
13.1%
3 69
 
9.8%
7 62
 
8.8%
5 61
 
8.7%
4 56
 
8.0%
0 52
 
7.4%
6 43
 
6.1%
9 29
 
4.1%
8 27
 
3.8%

법정동명
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
애월읍
279 
배동
148 
소초면
124 
남면
124 
금곡동
112 
Other values (27)
1108 

Length

Max length4
Median length3
Mean length2.83219
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
애월읍 279
14.7%
배동 148
 
7.8%
소초면 124
 
6.5%
남면 124
 
6.5%
금곡동 112
 
5.9%
만흥동 103
 
5.4%
삼산면 103
 
5.4%
효자동 88
 
4.6%
반포면 73
 
3.9%
설천면 58
 
3.1%
Other values (22) 683
36.0%

Length

2024-01-10T05:16:44.166907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
애월읍 279
14.7%
배동 148
 
7.8%
소초면 124
 
6.5%
남면 124
 
6.5%
금곡동 112
 
5.9%
만흥동 103
 
5.4%
삼산면 103
 
5.4%
효자동 88
 
4.6%
반포면 73
 
3.9%
설천면 58
 
3.1%
Other values (22) 683
36.0%

시도명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
전라남도
506 
제주특별자치도
279 
경상남도
241 
경상북도
200 
강원도
189 
Other values (5)
480 

Length

Max length7
Median length4
Mean length4.3546174
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 506
26.7%
제주특별자치도 279
14.7%
경상남도 241
12.7%
경상북도 200
 
10.6%
강원도 189
 
10.0%
광주광역시 112
 
5.9%
전라북도 99
 
5.2%
충청남도 94
 
5.0%
경기도 88
 
4.6%
충청북도 87
 
4.6%

Length

2024-01-10T05:16:44.291858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:16:44.412345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 506
26.7%
제주특별자치도 279
14.7%
경상남도 241
12.7%
경상북도 200
 
10.6%
강원도 189
 
10.0%
광주광역시 112
 
5.9%
전라북도 99
 
5.2%
충청남도 94
 
5.0%
경기도 88
 
4.6%
충청북도 87
 
4.6%

Interactions

2024-01-10T05:16:37.095463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:35.430264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:35.852473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:36.294714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:36.673891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:37.180151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:35.524929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:35.941912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:36.376664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:36.772989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:37.259272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:35.617602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:36.030351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:36.459116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:36.859424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:37.327750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:35.690453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:36.108149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:36.524693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:36.931222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:37.703649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:35.777161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:36.197930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:36.600039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:16:37.013860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:16:44.500930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설경도문화시설경도시설위도POI_ID문화시설위도국립공원명관광지리명시군구명국립공원지번주소법정동명시도명
시설경도1.0000.9610.9320.7990.9110.9581.0000.9901.0000.9940.947
문화시설경도0.9611.0000.9100.8040.9050.9320.9510.9760.9760.9730.938
시설위도0.9320.9101.0000.8480.9890.9891.0000.9951.0000.9980.988
POI_ID0.7990.8040.8481.0000.8170.9451.0000.9761.0000.9980.896
문화시설위도0.9110.9050.9890.8171.0000.9800.9840.9880.9880.9850.981
국립공원명0.9580.9320.9890.9450.9801.0001.0000.9991.0000.9980.996
관광지리명1.0000.9511.0001.0000.9841.0001.0001.0001.0001.0001.000
시군구명0.9900.9760.9950.9760.9880.9991.0001.0001.0000.9991.000
국립공원지번주소1.0000.9761.0001.0000.9881.0001.0001.0001.0001.0001.000
법정동명0.9940.9730.9980.9980.9850.9981.0000.9991.0001.0000.997
시도명0.9470.9380.9880.8960.9810.9961.0001.0001.0000.9971.000
2024-01-10T05:16:44.630597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관광지리명시군구명법정동명국립공원지번주소시도명국립공원명
관광지리명1.0000.9980.9991.0000.9930.996
시군구명0.9981.0000.9680.9990.9950.988
법정동명0.9990.9681.0000.9990.9700.961
국립공원지번주소1.0000.9990.9991.0000.9940.997
시도명0.9930.9950.9700.9941.0000.973
국립공원명0.9960.9880.9610.9970.9731.000
2024-01-10T05:16:44.764397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설경도문화시설경도시설위도POI_ID문화시설위도국립공원명관광지리명시군구명국립공원지번주소법정동명시도명
시설경도1.0000.9760.5180.1970.5170.7870.9940.9190.9940.9440.614
문화시설경도0.9761.0000.4870.1940.4910.7000.7490.8370.8360.8270.587
시설위도0.5180.4871.000-0.2320.9720.9300.9940.9550.9940.9770.807
POI_ID0.1970.194-0.2321.000-0.2360.7810.9910.8820.9930.9850.726
문화시설위도0.5170.4910.972-0.2361.0000.8810.8910.9030.9010.8870.756
국립공원명0.7870.7000.9300.7810.8811.0000.9960.9880.9970.9610.973
관광지리명0.9940.7490.9940.9910.8910.9961.0000.9981.0000.9990.993
시군구명0.9190.8370.9550.8820.9030.9880.9981.0000.9990.9680.995
국립공원지번주소0.9940.8360.9940.9930.9010.9971.0000.9991.0000.9990.994
법정동명0.9440.8270.9770.9850.8870.9610.9990.9680.9991.0000.970
시도명0.6140.5870.8070.7260.7560.9730.9930.9950.9940.9701.000

Missing values

2024-01-10T05:16:37.829821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:16:38.042723image/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.
2024-01-10T05:16:38.163628image/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

시설경도해발높이수건물번호문화시설경도문화시설명시설위도수정일자POI_ID고유ID번지번호문화시설위도국립공원명관광지리명문화시설격자코드시군구명국립공원지번주소도로명법정동명시도명
0129.215472<NA>15129.213194교보서적35.8010742023020711388603KCNCCPO22N000000058521-2735.864813경주국립공원<NA>마마546644경주시경상북도 경주시 배동 산49황성로1번길배동경상북도
1129.215472<NA>377129.20652교보서점35.8010742023020711388603KCNCCPO22N000000059715-1935.851341경주국립공원<NA>마마541629경주시경상북도 경주시 배동 산49금성로배동경상북도
2129.215472<NA>26268129.160849구산문화관35.8010742023020711388603KCNCCPO22N000000060164-135.890185경주국립공원<NA>마마499672경주시경상북도 경주시 배동 산49구산서원길배동경상북도
3129.215472<NA>17-36129.160554구토란요35.8010742023020711388603KCNCCPO22N000000061116635.752798경주국립공원<NA>마마501519경주시경상북도 경주시 배동 산49귀계길배동경상북도
4129.215472<NA>186129.227903국립경주박물관35.8010742023020711388603KCNCCPO22N0000000627635.828899경주국립공원<NA>마마560605경주시경상북도 경주시 배동 산49일정로배동경상북도
5129.215472<NA>284-3129.228851국립경주박물관직원주택35.8010742023020711388603KCNCCPO22N000000063389-1235.843517경주국립공원<NA>마마561621경주시경상북도 경주시 배동 산49알천남로배동경상북도
6129.215472<NA>186129.227901국립박물관문화재단35.8010742023020711388603KCNCCPO22N0000000647635.829362경주국립공원<NA>마마560605경주시경상북도 경주시 배동 산49일정로배동경상북도
7129.215472<NA>614129.28815국립정동극장35.8010742023020711388603KCNCCPO22N00000006513035.832154경주국립공원<NA>마마615609경주시경상북도 경주시 배동 산49경감로배동경상북도
8129.215472<NA>212129.224006국민서점35.8010742023020711388603KCNCCPO22N000000066652-635.841447경주국립공원<NA>마마556619경주시경상북도 경주시 배동 산49원효로배동경상북도
9129.215472<NA>05월 10일129.20535국제문화교류관35.8010742023020711388603KCNCCPO22N000000067101-135.833768경주국립공원<NA>마마540610경주시경상북도 경주시 배동 산49첨성로39번길배동경상북도
시설경도해발높이수건물번호문화시설경도문화시설명시설위도수정일자POI_ID고유ID번지번호문화시설위도국립공원명관광지리명문화시설격자코드시군구명국립공원지번주소도로명법정동명시도명
1885127.208543<NA>1239-7127.169584호반마을전시관36.37218720230207546279KCNCCPO22N000001705431-436.580977계룡산국립공원상신리다바704426공주시충청남도 공주시 반포면 상신리 산11-2의당전의로반포면충청남도
1886126.88906<NA>1126.84949CGV35.47946920230207546281KCNCCPO22N000001706525-135.56988내장산국립공원<NA>다마410306정읍시전라북도 정읍시 내장동 산231중앙1길내장동전라북도
1887126.88906<NA>174126.861397갤러리빌35.47946920230207546281KCNCCPO22N0000017071016-1235.583894내장산국립공원<NA>다마421321정읍시전라북도 정읍시 내장동 산231샘골로내장동전라북도
1888126.88906<NA><NA>126.857638경향신문정읍전단공사오리엔트기획35.47946920230207546281KCNCCPO22N00000170890-2935.568872내장산국립공원<NA>다마417305정읍시전라북도 정읍시 내장동 산231<NA>내장동전라북도
1889126.88906<NA>106126.887223광명서점35.47946920230207546281KCNCCPO22N000001709201-135.688507내장산국립공원<NA>다마445437정읍시전라북도 정읍시 내장동 산231신태인1길내장동전라북도
1890126.88906<NA>85126.849529교학서점35.47946920230207546281KCNCCPO22N00000171083935.5694내장산국립공원<NA>다마410305정읍시전라북도 정읍시 내장동 산231중앙로내장동전라북도
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