Overview

Dataset statistics

Number of variables10
Number of observations108
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.1 KiB
Average record size in memory86.2 B

Variable types

Categorical3
Text2
Numeric5

Dataset

DescriptionSample
Author㈜지오시스템리서치
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT09GSR004

Alerts

SIDO_NM is highly overall correlated with FCL_LC_LA and 2 other fieldsHigh correlation
SGG_NM is highly overall correlated with FCL_LC_LA and 2 other fieldsHigh correlation
FCL_LC_LA is highly overall correlated with SIDO_NM and 1 other fieldsHigh correlation
FCL_LC_LO is highly overall correlated with SIDO_NM and 1 other fieldsHigh correlation
FCL_WDTH is highly overall correlated with FCL_KND_NMHigh correlation
FCL_KND_NM is highly overall correlated with FCL_WDTHHigh correlation
FCL_PHOTO_FILE_NM has unique valuesUnique

Reproduction

Analysis started2024-03-13 12:34:02.230081
Analysis finished2024-03-13 12:34:07.763928
Duration5.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SIDO_NM
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size996.0 B
경상북도
46 
전라남도
22 
인천광역시
12 
전라북도
12 
충청남도
11 

Length

Max length5
Median length4
Mean length4.0648148
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 46
42.6%
전라남도 22
20.4%
인천광역시 12
 
11.1%
전라북도 12
 
11.1%
충청남도 11
 
10.2%
경기도 5
 
4.6%

Length

2024-03-13T21:34:07.885429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:34:08.101844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 46
42.6%
전라남도 22
20.4%
인천광역시 12
 
11.1%
전라북도 12
 
11.1%
충청남도 11
 
10.2%
경기도 5
 
4.6%

SGG_NM
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size996.0 B
울진군
13 
영덕군
13 
포항시
10 
경주시
10 
태안군
8 
Other values (16)
54 

Length

Max length3
Median length3
Mean length2.962963
Min length2

Unique

Unique2 ?
Unique (%)1.9%

Sample

1st row안산시
2nd row안산시
3rd row안산시
4th row화성시
5th row화성시

Common Values

ValueCountFrequency (%)
울진군 13
12.0%
영덕군 13
12.0%
포항시 10
 
9.3%
경주시 10
 
9.3%
태안군 8
 
7.4%
영광군 8
 
7.4%
옹진군 7
 
6.5%
고창군 5
 
4.6%
중구 4
 
3.7%
군산시 4
 
3.7%
Other values (11) 26
24.1%

Length

2024-03-13T21:34:08.332034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
울진군 13
12.0%
영덕군 13
12.0%
포항시 10
 
9.3%
경주시 10
 
9.3%
태안군 8
 
7.4%
영광군 8
 
7.4%
옹진군 7
 
6.5%
고창군 5
 
4.6%
군산시 4
 
3.7%
목포시 4
 
3.7%
Other values (11) 26
24.1%

TRGET_AREA_NM
Text

Distinct62
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Memory size996.0 B
2024-03-13T21:34:08.742782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.0833333
Min length2

Characters and Unicode

Total characters333
Distinct characters94
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)24.1%

Sample

1st row방아머리
2nd row서위
3rd row구봉도남측
4th row제부리
5th row제부리
ValueCountFrequency (%)
장사 4
 
3.7%
만리포 4
 
3.7%
송이도 3
 
2.8%
금곡~백석 3
 
2.8%
전촌·나정 3
 
2.8%
구시포 3
 
2.8%
두우리 3
 
2.8%
대천 3
 
2.8%
방망이섬 2
 
1.9%
무녀도 2
 
1.9%
Other values (52) 78
72.2%
2024-03-13T21:34:09.355676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 37
 
11.1%
포 21
 
6.3%
도 11
 
3.3%
~ 10
 
3.0%
대 9
 
2.7%
금 8
 
2.4%
장 6
 
1.8%
산 6
 
1.8%
위 6
 
1.8%
이 6
 
1.8%
Other values (84) 213
64.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 314
94.3%
Math Symbol 10
 
3.0%
Decimal Number 6
 
1.8%
Other Punctuation 3
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 37
 
11.8%
포 21
 
6.7%
도 11
 
3.5%
대 9
 
2.9%
금 8
 
2.5%
장 6
 
1.9%
산 6
 
1.9%
위 6
 
1.9%
이 6
 
1.9%
백 5
 
1.6%
Other values (80) 199
63.4%
Decimal Number
ValueCountFrequency (%)
2 4
66.7%
1 2
33.3%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%
Other Punctuation
ValueCountFrequency (%)
· 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 314
94.3%
Common 19
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 37
 
11.8%
포 21
 
6.7%
도 11
 
3.5%
대 9
 
2.9%
금 8
 
2.5%
장 6
 
1.9%
산 6
 
1.9%
위 6
 
1.9%
이 6
 
1.9%
백 5
 
1.6%
Other values (80) 199
63.4%
Common
ValueCountFrequency (%)
~ 10
52.6%
2 4
 
21.1%
· 3
 
15.8%
1 2
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 314
94.3%
ASCII 16
 
4.8%
None 3
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 37
 
11.8%
포 21
 
6.7%
도 11
 
3.5%
대 9
 
2.9%
금 8
 
2.5%
장 6
 
1.9%
산 6
 
1.9%
위 6
 
1.9%
이 6
 
1.9%
백 5
 
1.6%
Other values (80) 199
63.4%
ASCII
ValueCountFrequency (%)
~ 10
62.5%
2 4
 
25.0%
1 2
 
12.5%
None
ValueCountFrequency (%)
· 3
100.0%

FCL_KND_NM
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size996.0 B
석축호안
20 
계단식호안
20 
방파호안
18 
직립호안
16 
블록호안
6 
Other values (19)
28 

Length

Max length16
Median length4
Mean length4.6111111
Min length3

Unique

Unique14 ?
Unique (%)13.0%

Sample

1st row블록호안
2nd row블록호안
3rd row계단식호안
4th row직립호안
5th row석축호안

Common Values

ValueCountFrequency (%)
석축호안 20
18.5%
계단식호안 20
18.5%
방파호안 18
16.7%
직립호안 16
14.8%
블록호안 6
 
5.6%
EB블록호안 4
 
3.7%
산책로 3
 
2.8%
T.T.P. 설치구간 3
 
2.8%
경사호안 2
 
1.9%
해안도로 2
 
1.9%
Other values (14) 14
13.0%

Length

2024-03-13T21:34:09.570040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
석축호안 20
18.0%
계단식호안 20
18.0%
방파호안 18
16.2%
직립호안 16
14.4%
블록호안 6
 
5.4%
eb블록호안 4
 
3.6%
t.t.p 3
 
2.7%
설치구간 3
 
2.7%
산책로 3
 
2.7%
경사호안 2
 
1.8%
Other values (15) 16
14.4%

FCL_LC_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.161239
Minimum34.679436
Maximum37.591072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-13T21:34:09.780370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.679436
5-th percentile34.797592
Q135.565051
median36.236936
Q336.85003
95-th percentile37.284094
Maximum37.591072
Range2.9116361
Interquartile range (IQR)1.2849792

Descriptive statistics

Standard deviation0.81213141
Coefficient of variation (CV)0.022458617
Kurtosis-1.097592
Mean36.161239
Median Absolute Deviation (MAD)0.64928194
Skewness-0.15230256
Sum3905.4138
Variance0.65955743
MonotonicityNot monotonic
2024-03-13T21:34:09.994485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.67413333 2
 
1.9%
37.44746667 2
 
1.9%
35.50934167 1
 
0.9%
35.27698889 1
 
0.9%
35.27435278 1
 
0.9%
35.23720278 1
 
0.9%
35.230225 1
 
0.9%
35.22866944 1
 
0.9%
35.24426111 1
 
0.9%
35.24109444 1
 
0.9%
Other values (96) 96
88.9%
ValueCountFrequency (%)
34.67943611 1
0.9%
34.69338611 1
0.9%
34.73447222 1
0.9%
34.73510833 1
0.9%
34.77415 1
0.9%
34.77578889 1
0.9%
34.83808333 1
0.9%
34.83951389 1
0.9%
34.87161944 1
0.9%
34.886975 1
0.9%
ValueCountFrequency (%)
37.59107222 1
0.9%
37.44746667 2
1.9%
37.44455833 1
0.9%
37.38284167 1
0.9%
37.28594722 1
0.9%
37.28065278 1
0.9%
37.28019167 1
0.9%
37.27566389 1
0.9%
37.25006111 1
0.9%
37.22362778 1
0.9%

FCL_LC_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.7166
Minimum126.11132
Maximum129.5677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-13T21:34:10.212928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.11132
5-th percentile126.15182
Q1126.32182
median126.56994
Q3129.41651
95-th percentile129.49174
Maximum129.5677
Range3.4563806
Interquartile range (IQR)3.0946993

Descriptive statistics

Standard deviation1.5107146
Coefficient of variation (CV)0.011828647
Kurtosis-1.9200657
Mean127.7166
Median Absolute Deviation (MAD)0.4259375
Skewness0.22713319
Sum13793.393
Variance2.2822585
MonotonicityNot monotonic
2024-03-13T21:34:10.438722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.441275 2
 
1.9%
126.3726861 2
 
1.9%
126.4782639 1
 
0.9%
126.1529028 1
 
0.9%
126.1512389 1
 
0.9%
126.303375 1
 
0.9%
126.3002861 1
 
0.9%
126.3001528 1
 
0.9%
126.3069194 1
 
0.9%
126.30505 1
 
0.9%
Other values (96) 96
88.9%
ValueCountFrequency (%)
126.1113194 1
0.9%
126.1350972 1
0.9%
126.1418444 1
0.9%
126.1461556 1
0.9%
126.1463444 1
0.9%
126.1512389 1
0.9%
126.1529028 1
0.9%
126.1530417 1
0.9%
126.1716861 1
0.9%
126.1837694 1
0.9%
ValueCountFrequency (%)
129.5677 1
0.9%
129.5214861 1
0.9%
129.5183194 1
0.9%
129.4935583 1
0.9%
129.4928361 1
0.9%
129.4917583 1
0.9%
129.4917111 1
0.9%
129.4911528 1
0.9%
129.4758444 1
0.9%
129.4755917 1
0.9%

FCL_LT
Real number (ℝ)

Distinct84
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean364.73148
Minimum20
Maximum3100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-13T21:34:10.749242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile46.75
Q1111.25
median230
Q3480
95-th percentile1003.65
Maximum3100
Range3080
Interquartile range (IQR)368.75

Descriptive statistics

Standard deviation423.62021
Coefficient of variation (CV)1.1614578
Kurtosis18.14883
Mean364.73148
Median Absolute Deviation (MAD)140
Skewness3.5424136
Sum39391
Variance179454.09
MonotonicityNot monotonic
2024-03-13T21:34:11.031183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230 4
 
3.7%
170 3
 
2.8%
480 3
 
2.8%
90 3
 
2.8%
250 3
 
2.8%
300 2
 
1.9%
130 2
 
1.9%
400 2
 
1.9%
800 2
 
1.9%
135 2
 
1.9%
Other values (74) 82
75.9%
ValueCountFrequency (%)
20 2
1.9%
31 1
0.9%
35 1
0.9%
40 1
0.9%
45 1
0.9%
50 2
1.9%
52 1
0.9%
55 2
1.9%
60 2
1.9%
65 1
0.9%
ValueCountFrequency (%)
3100 1
0.9%
2200 1
0.9%
1170 1
0.9%
1100 1
0.9%
1040 1
0.9%
1011 1
0.9%
990 1
0.9%
850 1
0.9%
830 1
0.9%
800 2
1.9%

FCL_HGH
Real number (ℝ)

Distinct31
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3236111
Minimum0.5
Maximum5.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-13T21:34:11.295776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q11.5
median2
Q33
95-th percentile4.93
Maximum5.5
Range5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.1729717
Coefficient of variation (CV)0.5048055
Kurtosis-0.099300355
Mean2.3236111
Median Absolute Deviation (MAD)0.8
Skewness0.74235258
Sum250.95
Variance1.3758625
MonotonicityNot monotonic
2024-03-13T21:34:11.530247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1.0 15
13.9%
1.5 11
10.2%
2.5 11
10.2%
2.0 11
10.2%
3.0 9
 
8.3%
4.0 8
 
7.4%
5.0 5
 
4.6%
3.5 4
 
3.7%
1.8 4
 
3.7%
2.8 3
 
2.8%
Other values (21) 27
25.0%
ValueCountFrequency (%)
0.5 2
 
1.9%
0.7 1
 
0.9%
0.8 1
 
0.9%
1.0 15
13.9%
1.2 2
 
1.9%
1.25 1
 
0.9%
1.3 2
 
1.9%
1.4 2
 
1.9%
1.5 11
10.2%
1.6 2
 
1.9%
ValueCountFrequency (%)
5.5 1
 
0.9%
5.0 5
4.6%
4.8 1
 
0.9%
4.0 8
7.4%
3.8 1
 
0.9%
3.6 1
 
0.9%
3.5 4
3.7%
3.2 1
 
0.9%
3.0 9
8.3%
2.9 1
 
0.9%

FCL_WDTH
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5083333
Minimum0.3
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-13T21:34:11.734851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.8
Q11
median1.65
Q33
95-th percentile6.825
Maximum30
Range29.7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.2380369
Coefficient of variation (CV)1.2909117
Kurtosis48.870511
Mean2.5083333
Median Absolute Deviation (MAD)0.65
Skewness6.0887174
Sum270.9
Variance10.484883
MonotonicityNot monotonic
2024-03-13T21:34:11.935215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1.0 33
30.6%
2.0 18
16.7%
1.5 9
 
8.3%
3.0 9
 
8.3%
0.8 8
 
7.4%
4.0 5
 
4.6%
5.0 4
 
3.7%
3.5 3
 
2.8%
0.5 3
 
2.8%
1.8 3
 
2.8%
Other values (11) 13
 
12.0%
ValueCountFrequency (%)
0.3 1
 
0.9%
0.5 3
 
2.8%
0.8 8
 
7.4%
1.0 33
30.6%
1.5 9
 
8.3%
1.8 3
 
2.8%
2.0 18
16.7%
2.5 1
 
0.9%
3.0 9
 
8.3%
3.3 1
 
0.9%
ValueCountFrequency (%)
30.0 1
 
0.9%
10.0 1
 
0.9%
8.0 1
 
0.9%
7.6 1
 
0.9%
7.0 2
 
1.9%
6.5 1
 
0.9%
6.0 2
 
1.9%
5.0 4
3.7%
4.0 5
4.6%
3.5 3
2.8%

FCL_PHOTO_FILE_NM
Text

UNIQUE 

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size996.0 B
2024-03-13T21:34:12.337356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length20.5
Min length19

Characters and Unicode

Total characters2214
Distinct characters113
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)100.0%

Sample

1st row20210401_방아머리_0164.JPG
2nd row20210401_서위_0405.JPG
3rd row20210401_구봉도남측_0231.JPG
4th row20210402_제부리_0110.JPG
5th row20210906_제부리_373.JPG
ValueCountFrequency (%)
20210401_방아머리_0164.jpg 1
 
0.9%
20210413_모항_0044.jpg 1
 
0.9%
20210419_두우리_556.jpg 1
 
0.9%
20210419_두우리_134.jpg 1
 
0.9%
20210419_두우리_020.jpg 1
 
0.9%
20210423_백바위_0664.jpg 1
 
0.9%
20210423_백바위_0084.jpg 1
 
0.9%
20210521_구시포_0699.jpg 1
 
0.9%
20210521_구시포_1147.jpg 1
 
0.9%
20210521_구시포_1272.jpg 1
 
0.9%
Other values (98) 98
90.7%
2024-03-13T21:34:13.024580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 377
17.0%
2 298
13.5%
_ 213
 
9.6%
1 211
 
9.5%
. 108
 
4.9%
4 98
 
4.4%
J 96
 
4.3%
P 96
 
4.3%
G 96
 
4.3%
6 69
 
3.1%
Other values (103) 552
24.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1254
56.6%
Other Letter 305
 
13.8%
Uppercase Letter 290
 
13.1%
Connector Punctuation 213
 
9.6%
Other Punctuation 108
 
4.9%
Lowercase Letter 36
 
1.6%
Math Symbol 4
 
0.2%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 31
 
10.2%
포 19
 
6.2%
도 11
 
3.6%
대 9
 
3.0%
금 8
 
2.6%
장 6
 
2.0%
산 6
 
2.0%
이 6
 
2.0%
위 6
 
2.0%
호 5
 
1.6%
Other values (80) 198
64.9%
Decimal Number
ValueCountFrequency (%)
0 377
30.1%
2 298
23.8%
1 211
16.8%
4 98
 
7.8%
6 69
 
5.5%
5 54
 
4.3%
3 45
 
3.6%
7 37
 
3.0%
8 34
 
2.7%
9 31
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
J 96
33.1%
P 96
33.1%
G 96
33.1%
A 1
 
0.3%
B 1
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
g 12
33.3%
j 12
33.3%
p 12
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 213
100.0%
Other Punctuation
ValueCountFrequency (%)
. 108
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1583
71.5%
Latin 326
 
14.7%
Hangul 305
 
13.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 31
 
10.2%
포 19
 
6.2%
도 11
 
3.6%
대 9
 
3.0%
금 8
 
2.6%
장 6
 
2.0%
산 6
 
2.0%
이 6
 
2.0%
위 6
 
2.0%
호 5
 
1.6%
Other values (80) 198
64.9%
Common
ValueCountFrequency (%)
0 377
23.8%
2 298
18.8%
_ 213
13.5%
1 211
13.3%
. 108
 
6.8%
4 98
 
6.2%
6 69
 
4.4%
5 54
 
3.4%
3 45
 
2.8%
7 37
 
2.3%
Other values (5) 73
 
4.6%
Latin
ValueCountFrequency (%)
J 96
29.4%
P 96
29.4%
G 96
29.4%
g 12
 
3.7%
j 12
 
3.7%
p 12
 
3.7%
A 1
 
0.3%
B 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1909
86.2%
Hangul 305
 
13.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 377
19.7%
2 298
15.6%
_ 213
11.2%
1 211
11.1%
. 108
 
5.7%
4 98
 
5.1%
J 96
 
5.0%
P 96
 
5.0%
G 96
 
5.0%
6 69
 
3.6%
Other values (13) 247
12.9%
Hangul
ValueCountFrequency (%)
리 31
 
10.2%
포 19
 
6.2%
도 11
 
3.6%
대 9
 
3.0%
금 8
 
2.6%
장 6
 
2.0%
산 6
 
2.0%
이 6
 
2.0%
위 6
 
2.0%
호 5
 
1.6%
Other values (80) 198
64.9%

Interactions

2024-03-13T21:34:06.013787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:02.951299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:03.742297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:04.523080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:05.231461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:06.211993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:03.071653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:03.896184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:04.687935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:05.423054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:06.398701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:03.230987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:04.112897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:04.823678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:05.587597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:06.547228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:03.368712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:04.248942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:04.973185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:05.704911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:06.662719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:03.549508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:04.382492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:05.106070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:34:05.843551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:34:13.180842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIDO_NMSGG_NMTRGET_AREA_NMFCL_KND_NMFCL_LC_LAFCL_LC_LOFCL_LTFCL_HGHFCL_WDTH
SIDO_NM1.0001.0000.9990.6570.8650.7340.3460.5010.167
SGG_NM1.0001.0000.9990.0000.9720.9920.1920.4390.427
TRGET_AREA_NM0.9990.9991.0000.0000.9980.9690.8140.5870.964
FCL_KND_NM0.6570.0000.0001.0000.4260.6620.4220.0000.907
FCL_LC_LA0.8650.9720.9980.4261.0000.7780.1780.6120.252
FCL_LC_LO0.7340.9920.9690.6620.7781.0000.0000.0000.000
FCL_LT0.3460.1920.8140.4220.1780.0001.0000.0930.615
FCL_HGH0.5010.4390.5870.0000.6120.0000.0931.0000.000
FCL_WDTH0.1670.4270.9640.9070.2520.0000.6150.0001.000
2024-03-13T21:34:13.445444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
FCL_KND_NMSIDO_NMSGG_NM
FCL_KND_NM1.0000.2940.000
SIDO_NM0.2941.0000.924
SGG_NM0.0000.9241.000
2024-03-13T21:34:13.584984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
FCL_LC_LAFCL_LC_LOFCL_LTFCL_HGHFCL_WDTHSIDO_NMSGG_NMFCL_KND_NM
FCL_LC_LA1.0000.0410.0680.1650.1420.6760.7930.151
FCL_LC_LO0.0411.0000.0660.0670.0200.6010.8910.352
FCL_LT0.0680.0661.0000.074-0.0710.1290.0680.160
FCL_HGH0.1650.0670.0741.0000.2000.2240.1900.000
FCL_WDTH0.1420.020-0.0710.2001.0000.1110.2030.647
SIDO_NM0.6760.6010.1290.2240.1111.0000.9240.294
SGG_NM0.7930.8910.0680.1900.2030.9241.0000.000
FCL_KND_NM0.1510.3520.1600.0000.6470.2940.0001.000

Missing values

2024-03-13T21:34:07.395741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:34:07.657259image/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

SIDO_NMSGG_NMTRGET_AREA_NMFCL_KND_NMFCL_LC_LAFCL_LC_LOFCL_LTFCL_HGHFCL_WDTHFCL_PHOTO_FILE_NM
0경기도안산시방아머리블록호안37.285947126.5719836503.55.020210401_방아머리_0164.JPG
1경기도안산시서위블록호안37.280653126.5678922502.65.020210401_서위_0405.JPG
2경기도안산시구봉도남측계단식호안37.280192126.547744505.03.020210401_구봉도남측_0231.JPG
3경기도화성시제부리직립호안37.162728126.6211365903.51.020210402_제부리_0110.JPG
4경기도화성시제부리석축호안37.166214126.6173838503.01.020210906_제부리_373.JPG
5인천광역시강화군동막직립호안37.591072126.4608221001.51.020210412_동막_0069.JPG
6인천광역시중구을왕해안로37.444558126.3721223031.55.020210413_을왕_0003.JPG
7인천광역시중구을왕계단식호안37.447467126.3726861561.73.020210413_을왕_0300.JPG
8인천광역시중구선녀바위석축호안37.447467126.372686451.52.020210413_선녀바위_0295.JPG
9인천광역시중구하나개석축호안37.382842126.409944781.71.020210412_하나개_0035.JPG
SIDO_NMSGG_NMTRGET_AREA_NMFCL_KND_NMFCL_LC_LAFCL_LC_LOFCL_LTFCL_HGHFCL_WDTHFCL_PHOTO_FILE_NM
98충청남도태안군학암포해안산책로36.898764126.2128752431.01.020210426_학암포_457.JPG
99충청남도태안군신두리석축호안36.836833126.1837696635.51.020210427_신두리_0282.JPG
100충청남도태안군신두리계단식호안36.840092126.1892781311.21.820210427_신두리_0588.JPG
101충청남도태안군만리포직립호안36.794197126.1463441402.40.320210428_만리포_1499.JPG
102충청남도태안군만리포석축호안36.790806126.1461563002.31.020210428_만리포_1198.JPG
103충청남도태안군만리포계단식호안36.785144126.1418441801.01.020210428_만리포_0710.JPG
104충청남도태안군만리포목책데크36.782722126.13509710111.02.520210428_만리포_0237.JPG
105충청남도보령시대천계단식호안36.301325126.5181941922.87.620210520_대천_0070.JPG
106충청남도보령시대천완경사호안36.303228126.5172282881.06.520210520_대천_0218.JPG
107충청남도보령시대천직립호안36.3125126.512311701.01.020210520_대천_1095.JPG