Overview

Dataset statistics

Number of variables15
Number of observations5933
Missing cells19629
Missing cells (%)22.1%
Duplicate rows4
Duplicate rows (%)0.1%
Total size in memory730.2 KiB
Average record size in memory126.0 B

Variable types

Categorical2
Text6
Numeric6
DateTime1

Alerts

Dataset has 4 (0.1%) duplicate rowsDuplicates
관리기관전화번호 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
시군명 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
위도 is highly overall correlated with 염화칼슘비치량 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 모래비치량 and 2 other fieldsHigh correlation
제설함수(개) is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
염화칼슘비치량 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
모래비치량 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
읍면동명 has 2144 (36.1%) missing valuesMissing
관리번호 has 747 (12.6%) missing valuesMissing
설치장소명 has 2520 (42.5%) missing valuesMissing
소재지도로명주소 has 2579 (43.5%) missing valuesMissing
염화칼슘비치량 has 3633 (61.2%) missing valuesMissing
소금비치량 has 4369 (73.6%) missing valuesMissing
모래비치량 has 3637 (61.3%) missing valuesMissing
제설함수(개) is highly skewed (γ1 = 29.90307971)Skewed
염화칼슘비치량 has 252 (4.2%) zerosZeros
소금비치량 has 1533 (25.8%) zerosZeros
모래비치량 has 949 (16.0%) zerosZeros

Reproduction

Analysis started2024-04-29 13:28:02.149651
Analysis finished2024-04-29 13:28:09.468498
Duration7.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size46.5 KiB
수원시
1564 
평택시
902 
부천시
839 
고양시
340 
남양주시
260 
Other values (21)
2028 

Length

Max length4
Median length3
Mean length3.0790494
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
수원시 1564
26.4%
평택시 902
15.2%
부천시 839
14.1%
고양시 340
 
5.7%
남양주시 260
 
4.4%
군포시 234
 
3.9%
안양시 213
 
3.6%
화성시 202
 
3.4%
안산시 196
 
3.3%
성남시 175
 
2.9%
Other values (16) 1008
17.0%

Length

2024-04-29T22:28:09.539073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 1564
26.4%
평택시 902
15.2%
부천시 839
14.1%
고양시 340
 
5.7%
남양주시 260
 
4.4%
군포시 234
 
3.9%
안양시 213
 
3.6%
화성시 202
 
3.4%
안산시 196
 
3.3%
성남시 175
 
2.9%
Other values (16) 1008
17.0%

읍면동명
Text

MISSING 

Distinct253
Distinct (%)6.7%
Missing2144
Missing (%)36.1%
Memory size46.5 KiB
2024-04-29T22:28:09.822658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.2565321
Min length2

Characters and Unicode

Total characters12339
Distinct characters162
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

Unique36 ?
Unique (%)1.0%

Sample

1st row청평면
2nd row가평읍
3rd row가평읍
4th row가평읍
5th row가평읍
ValueCountFrequency (%)
진위면 116
 
3.1%
포승읍 96
 
2.5%
서탄면 82
 
2.2%
안중읍 80
 
2.1%
부천동 78
 
2.1%
지산동 77
 
2.0%
서정동 76
 
2.0%
신장동 74
 
2.0%
성곡동 73
 
1.9%
군포1동 72
 
1.9%
Other values (243) 2965
78.3%
2024-04-29T22:28:10.222886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 2968
24.1%
면 449
 
3.6%
읍 441
 
3.6%
1 366
 
3.0%
곡 298
 
2.4%
산 261
 
2.1%
본 257
 
2.1%
안 224
 
1.8%
2 223
 
1.8%
신 212
 
1.7%
Other values (152) 6640
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11599
94.0%
Decimal Number 739
 
6.0%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2968
25.6%
면 449
 
3.9%
읍 441
 
3.8%
곡 298
 
2.6%
산 261
 
2.3%
본 257
 
2.2%
안 224
 
1.9%
신 212
 
1.8%
중 205
 
1.8%
포 203
 
1.8%
Other values (143) 6081
52.4%
Decimal Number
ValueCountFrequency (%)
1 366
49.5%
2 223
30.2%
3 101
 
13.7%
9 13
 
1.8%
5 11
 
1.5%
8 9
 
1.2%
6 8
 
1.1%
7 8
 
1.1%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11599
94.0%
Common 740
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2968
25.6%
면 449
 
3.9%
읍 441
 
3.8%
곡 298
 
2.6%
산 261
 
2.3%
본 257
 
2.2%
안 224
 
1.9%
신 212
 
1.8%
중 205
 
1.8%
포 203
 
1.8%
Other values (143) 6081
52.4%
Common
ValueCountFrequency (%)
1 366
49.5%
2 223
30.1%
3 101
 
13.6%
9 13
 
1.8%
5 11
 
1.5%
8 9
 
1.2%
6 8
 
1.1%
7 8
 
1.1%
? 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11599
94.0%
ASCII 740
 
6.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 2968
25.6%
면 449
 
3.9%
읍 441
 
3.8%
곡 298
 
2.6%
산 261
 
2.3%
본 257
 
2.2%
안 224
 
1.9%
신 212
 
1.8%
중 205
 
1.8%
포 203
 
1.8%
Other values (143) 6081
52.4%
ASCII
ValueCountFrequency (%)
1 366
49.5%
2 223
30.1%
3 101
 
13.6%
9 13
 
1.8%
5 11
 
1.5%
8 9
 
1.2%
6 8
 
1.1%
7 8
 
1.1%
? 1
 
0.1%

관리번호
Text

MISSING 

Distinct4619
Distinct (%)89.1%
Missing747
Missing (%)12.6%
Memory size46.5 KiB
2024-04-29T22:28:10.559292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length5.9112997
Min length1

Characters and Unicode

Total characters30656
Distinct characters152
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4314 ?
Unique (%)83.2%

Sample

1st row17
2nd row10
3rd row8
4th row5
5th row7
ValueCountFrequency (%)
1 18
 
0.3%
2 16
 
0.3%
4 15
 
0.3%
3 15
 
0.3%
5 12
 
0.2%
6 11
 
0.2%
8 10
 
0.2%
10 9
 
0.2%
12 8
 
0.2%
11 8
 
0.2%
Other values (4609) 5064
97.6%
2024-04-29T22:28:11.023155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 3954
 
12.9%
1 2945
 
9.6%
0 2176
 
7.1%
2 2155
 
7.0%
동 1925
 
6.3%
3 1643
 
5.4%
4 1278
 
4.2%
5 1018
 
3.3%
6 912
 
3.0%
7 816
 
2.7%
Other values (142) 11834
38.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14410
47.0%
Other Letter 12262
40.0%
Dash Punctuation 3954
 
12.9%
Math Symbol 29
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1925
 
15.7%
영 681
 
5.6%
통 676
 
5.5%
광 329
 
2.7%
선 282
 
2.3%
천 276
 
2.3%
본 257
 
2.1%
곡 256
 
2.1%
권 226
 
1.8%
교 210
 
1.7%
Other values (129) 7144
58.3%
Decimal Number
ValueCountFrequency (%)
1 2945
20.4%
0 2176
15.1%
2 2155
15.0%
3 1643
11.4%
4 1278
8.9%
5 1018
 
7.1%
6 912
 
6.3%
7 816
 
5.7%
8 745
 
5.2%
9 722
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 3954
100.0%
Math Symbol
ValueCountFrequency (%)
~ 29
100.0%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18394
60.0%
Hangul 12262
40.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1925
 
15.7%
영 681
 
5.6%
통 676
 
5.5%
광 329
 
2.7%
선 282
 
2.3%
천 276
 
2.3%
본 257
 
2.1%
곡 256
 
2.1%
권 226
 
1.8%
교 210
 
1.7%
Other values (129) 7144
58.3%
Common
ValueCountFrequency (%)
- 3954
21.5%
1 2945
16.0%
0 2176
11.8%
2 2155
11.7%
3 1643
8.9%
4 1278
 
6.9%
5 1018
 
5.5%
6 912
 
5.0%
7 816
 
4.4%
8 745
 
4.1%
Other values (3) 752
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18394
60.0%
Hangul 12262
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 3954
21.5%
1 2945
16.0%
0 2176
11.8%
2 2155
11.7%
3 1643
8.9%
4 1278
 
6.9%
5 1018
 
5.5%
6 912
 
5.0%
7 816
 
4.4%
8 745
 
4.1%
Other values (3) 752
 
4.1%
Hangul
ValueCountFrequency (%)
동 1925
 
15.7%
영 681
 
5.6%
통 676
 
5.5%
광 329
 
2.7%
선 282
 
2.3%
천 276
 
2.3%
본 257
 
2.1%
곡 256
 
2.1%
권 226
 
1.8%
교 210
 
1.7%
Other values (129) 7144
58.3%

설치장소명
Text

MISSING 

Distinct2848
Distinct (%)83.4%
Missing2520
Missing (%)42.5%
Memory size46.5 KiB
2024-04-29T22:28:11.294532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length28
Mean length9.3803106
Min length3

Characters and Unicode

Total characters32015
Distinct characters551
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2683 ?
Unique (%)78.6%

Sample

1st row고성리
2nd row금대리
3rd row달전2리
4th row달전리
5th row두밀리
ValueCountFrequency (%)
앞 393
 
5.6%
소사본동 79
 
1.1%
입구 76
 
1.1%
사거리 71
 
1.0%
송내동 70
 
1.0%
심곡본동 68
 
1.0%
삼거리 58
 
0.8%
정문 56
 
0.8%
고강동 52
 
0.7%
괴안동 51
 
0.7%
Other values (3407) 6003
86.0%
2024-04-29T22:28:11.693565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3581
 
11.2%
동 1381
 
4.3%
1 1245
 
3.9%
- 951
 
3.0%
리 933
 
2.9%
2 902
 
2.8%
로 806
 
2.5%
3 748
 
2.3%
4 603
 
1.9%
5 563
 
1.8%
Other values (541) 20302
63.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20078
62.7%
Decimal Number 6243
 
19.5%
Space Separator 3581
 
11.2%
Dash Punctuation 951
 
3.0%
Close Punctuation 436
 
1.4%
Open Punctuation 433
 
1.4%
Uppercase Letter 145
 
0.5%
Math Symbol 56
 
0.2%
Other Punctuation 47
 
0.1%
Other Number 32
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1381
 
6.9%
리 933
 
4.6%
로 806
 
4.0%
앞 479
 
2.4%
사 413
 
2.1%
교 409
 
2.0%
고 370
 
1.8%
산 341
 
1.7%
지 335
 
1.7%
거 318
 
1.6%
Other values (491) 14293
71.2%
Uppercase Letter
ValueCountFrequency (%)
C 32
22.1%
I 29
20.0%
A 14
9.7%
T 12
 
8.3%
P 11
 
7.6%
S 10
 
6.9%
G 9
 
6.2%
K 6
 
4.1%
L 5
 
3.4%
B 4
 
2.8%
Other values (8) 13
9.0%
Decimal Number
ValueCountFrequency (%)
1 1245
19.9%
2 902
14.4%
3 748
12.0%
4 603
9.7%
5 563
9.0%
6 495
 
7.9%
7 458
 
7.3%
0 451
 
7.2%
9 392
 
6.3%
8 386
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 20
42.6%
@ 13
27.7%
# 10
21.3%
/ 1
 
2.1%
. 1
 
2.1%
: 1
 
2.1%
? 1
 
2.1%
Lowercase Letter
ValueCountFrequency (%)
e 5
38.5%
i 3
23.1%
c 3
23.1%
g 1
 
7.7%
s 1
 
7.7%
Other Number
ValueCountFrequency (%)
② 15
46.9%
① 13
40.6%
③ 3
 
9.4%
④ 1
 
3.1%
Math Symbol
ValueCountFrequency (%)
~ 49
87.5%
↔ 7
 
12.5%
Space Separator
ValueCountFrequency (%)
3581
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 951
100.0%
Close Punctuation
ValueCountFrequency (%)
) 436
100.0%
Open Punctuation
ValueCountFrequency (%)
( 433
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20077
62.7%
Common 11779
36.8%
Latin 158
 
0.5%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1381
 
6.9%
리 933
 
4.6%
로 806
 
4.0%
앞 479
 
2.4%
사 413
 
2.1%
교 409
 
2.0%
고 370
 
1.8%
산 341
 
1.7%
지 335
 
1.7%
거 318
 
1.6%
Other values (490) 14292
71.2%
Common
ValueCountFrequency (%)
3581
30.4%
1 1245
 
10.6%
- 951
 
8.1%
2 902
 
7.7%
3 748
 
6.4%
4 603
 
5.1%
5 563
 
4.8%
6 495
 
4.2%
7 458
 
3.9%
0 451
 
3.8%
Other values (17) 1782
15.1%
Latin
ValueCountFrequency (%)
C 32
20.3%
I 29
18.4%
A 14
8.9%
T 12
 
7.6%
P 11
 
7.0%
S 10
 
6.3%
G 9
 
5.7%
K 6
 
3.8%
e 5
 
3.2%
L 5
 
3.2%
Other values (13) 25
15.8%
Han
ValueCountFrequency (%)
下 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20077
62.7%
ASCII 11898
37.2%
Enclosed Alphanum 32
 
0.1%
Arrows 7
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3581
30.1%
1 1245
 
10.5%
- 951
 
8.0%
2 902
 
7.6%
3 748
 
6.3%
4 603
 
5.1%
5 563
 
4.7%
6 495
 
4.2%
7 458
 
3.8%
0 451
 
3.8%
Other values (35) 1901
16.0%
Hangul
ValueCountFrequency (%)
동 1381
 
6.9%
리 933
 
4.6%
로 806
 
4.0%
앞 479
 
2.4%
사 413
 
2.1%
교 409
 
2.0%
고 370
 
1.8%
산 341
 
1.7%
지 335
 
1.7%
거 318
 
1.6%
Other values (490) 14292
71.2%
Enclosed Alphanum
ValueCountFrequency (%)
② 15
46.9%
① 13
40.6%
③ 3
 
9.4%
④ 1
 
3.1%
Arrows
ValueCountFrequency (%)
↔ 7
100.0%
CJK
ValueCountFrequency (%)
下 1
100.0%

소재지도로명주소
Text

MISSING 

Distinct2825
Distinct (%)84.2%
Missing2579
Missing (%)43.5%
Memory size46.5 KiB
2024-04-29T22:28:11.989596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length38
Mean length20.148181
Min length11

Characters and Unicode

Total characters67577
Distinct characters359
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2524 ?
Unique (%)75.3%

Sample

1st row경기도 가평군 청평면 말래골길 6
2nd row경기도 가평군 가평읍 북한강변로 320-16
3rd row경기도 가평군 가평읍 강산길 14
4th row경기도 가평군 가평읍 북한강변로 1127-1
5th row경기도 가평군 가평읍 태봉두밀로 422-5
ValueCountFrequency (%)
경기도 3354
 
21.0%
수원시 911
 
5.7%
부천시 684
 
4.3%
평택시 348
 
2.2%
고양시 340
 
2.1%
소사구 285
 
1.8%
권선구 249
 
1.6%
오정구 248
 
1.6%
장안구 244
 
1.5%
군포시 233
 
1.5%
Other values (2757) 9045
56.7%
2024-04-29T22:28:12.418075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12650
18.7%
경 3511
 
5.2%
도 3406
 
5.0%
기 3386
 
5.0%
시 3371
 
5.0%
로 2889
 
4.3%
1 2205
 
3.3%
구 2148
 
3.2%
2 1619
 
2.4%
길 1524
 
2.3%
Other values (349) 30868
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42244
62.5%
Space Separator 12650
 
18.7%
Decimal Number 11735
 
17.4%
Dash Punctuation 656
 
1.0%
Other Punctuation 104
 
0.2%
Open Punctuation 92
 
0.1%
Close Punctuation 92
 
0.1%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 3511
 
8.3%
도 3406
 
8.1%
기 3386
 
8.0%
시 3371
 
8.0%
로 2889
 
6.8%
구 2148
 
5.1%
길 1524
 
3.6%
원 1322
 
3.1%
번 1238
 
2.9%
수 1192
 
2.8%
Other values (332) 18257
43.2%
Decimal Number
ValueCountFrequency (%)
1 2205
18.8%
2 1619
13.8%
3 1345
11.5%
4 1197
10.2%
5 1118
9.5%
6 1005
8.6%
7 840
 
7.2%
0 831
 
7.1%
9 796
 
6.8%
8 779
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%
Space Separator
ValueCountFrequency (%)
12650
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 656
100.0%
Other Punctuation
ValueCountFrequency (%)
, 104
100.0%
Open Punctuation
ValueCountFrequency (%)
( 92
100.0%
Close Punctuation
ValueCountFrequency (%)
) 92
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42244
62.5%
Common 25329
37.5%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 3511
 
8.3%
도 3406
 
8.1%
기 3386
 
8.0%
시 3371
 
8.0%
로 2889
 
6.8%
구 2148
 
5.1%
길 1524
 
3.6%
원 1322
 
3.1%
번 1238
 
2.9%
수 1192
 
2.8%
Other values (332) 18257
43.2%
Common
ValueCountFrequency (%)
12650
49.9%
1 2205
 
8.7%
2 1619
 
6.4%
3 1345
 
5.3%
4 1197
 
4.7%
5 1118
 
4.4%
6 1005
 
4.0%
7 840
 
3.3%
0 831
 
3.3%
9 796
 
3.1%
Other values (5) 1723
 
6.8%
Latin
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42244
62.5%
ASCII 25333
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12650
49.9%
1 2205
 
8.7%
2 1619
 
6.4%
3 1345
 
5.3%
4 1197
 
4.7%
5 1118
 
4.4%
6 1005
 
4.0%
7 840
 
3.3%
0 831
 
3.3%
9 796
 
3.1%
Other values (7) 1727
 
6.8%
Hangul
ValueCountFrequency (%)
경 3511
 
8.3%
도 3406
 
8.1%
기 3386
 
8.0%
시 3371
 
8.0%
로 2889
 
6.8%
구 2148
 
5.1%
길 1524
 
3.6%
원 1322
 
3.1%
번 1238
 
2.9%
수 1192
 
2.8%
Other values (332) 18257
43.2%

소재지지번주소
Text

Distinct4979
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Memory size46.5 KiB
2024-04-29T22:28:12.710381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length39
Mean length21.618743
Min length13

Characters and Unicode

Total characters128264
Distinct characters456
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

Unique4463 ?
Unique (%)75.2%

Sample

1st row경기도 가평군 청평면 고성리 437-1번지
2nd row경기도 가평군 가평읍 금대리 140번지
3rd row경기도 가평군 가평읍 달전리 454번지
4th row경기도 가평군 가평읍 달전리 312-1번지
5th row경기도 가평군 가평읍 두밀리 44-6번지
ValueCountFrequency (%)
경기도 5933
 
20.4%
수원시 1562
 
5.4%
평택시 902
 
3.1%
부천시 839
 
2.9%
영통구 496
 
1.7%
권선구 405
 
1.4%
장안구 353
 
1.2%
소사구 352
 
1.2%
고양시 340
 
1.2%
팔달구 308
 
1.1%
Other values (5909) 17642
60.6%
2024-04-29T22:28:13.168772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23199
 
18.1%
도 6061
 
4.7%
기 6001
 
4.7%
경 5948
 
4.6%
시 5918
 
4.6%
동 5336
 
4.2%
1 4837
 
3.8%
- 4336
 
3.4%
구 3541
 
2.8%
2 2956
 
2.3%
Other values (446) 60131
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76712
59.8%
Decimal Number 23722
 
18.5%
Space Separator 23199
 
18.1%
Dash Punctuation 4336
 
3.4%
Close Punctuation 127
 
0.1%
Open Punctuation 127
 
0.1%
Uppercase Letter 34
 
< 0.1%
Other Punctuation 5
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 6061
 
7.9%
기 6001
 
7.8%
경 5948
 
7.8%
시 5918
 
7.7%
동 5336
 
7.0%
구 3541
 
4.6%
지 2466
 
3.2%
원 2278
 
3.0%
번 2217
 
2.9%
수 1906
 
2.5%
Other values (412) 35040
45.7%
Uppercase Letter
ValueCountFrequency (%)
N 5
14.7%
G 4
11.8%
L 3
 
8.8%
I 2
 
5.9%
O 2
 
5.9%
C 2
 
5.9%
T 2
 
5.9%
A 2
 
5.9%
S 2
 
5.9%
H 2
 
5.9%
Other values (6) 8
23.5%
Decimal Number
ValueCountFrequency (%)
1 4837
20.4%
2 2956
12.5%
3 2578
10.9%
4 2238
9.4%
5 2223
9.4%
6 1946
8.2%
7 1840
 
7.8%
8 1711
 
7.2%
9 1701
 
7.2%
0 1692
 
7.1%
Other Punctuation
ValueCountFrequency (%)
, 3
60.0%
: 1
 
20.0%
. 1
 
20.0%
Space Separator
ValueCountFrequency (%)
23199
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4336
100.0%
Close Punctuation
ValueCountFrequency (%)
) 127
100.0%
Open Punctuation
ValueCountFrequency (%)
( 127
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76712
59.8%
Common 51518
40.2%
Latin 34
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 6061
 
7.9%
기 6001
 
7.8%
경 5948
 
7.8%
시 5918
 
7.7%
동 5336
 
7.0%
구 3541
 
4.6%
지 2466
 
3.2%
원 2278
 
3.0%
번 2217
 
2.9%
수 1906
 
2.5%
Other values (412) 35040
45.7%
Common
ValueCountFrequency (%)
23199
45.0%
1 4837
 
9.4%
- 4336
 
8.4%
2 2956
 
5.7%
3 2578
 
5.0%
4 2238
 
4.3%
5 2223
 
4.3%
6 1946
 
3.8%
7 1840
 
3.6%
8 1711
 
3.3%
Other values (8) 3654
 
7.1%
Latin
ValueCountFrequency (%)
N 5
14.7%
G 4
11.8%
L 3
 
8.8%
I 2
 
5.9%
O 2
 
5.9%
C 2
 
5.9%
T 2
 
5.9%
A 2
 
5.9%
S 2
 
5.9%
H 2
 
5.9%
Other values (6) 8
23.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76712
59.8%
ASCII 51552
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23199
45.0%
1 4837
 
9.4%
- 4336
 
8.4%
2 2956
 
5.7%
3 2578
 
5.0%
4 2238
 
4.3%
5 2223
 
4.3%
6 1946
 
3.8%
7 1840
 
3.6%
8 1711
 
3.3%
Other values (24) 3688
 
7.2%
Hangul
ValueCountFrequency (%)
도 6061
 
7.9%
기 6001
 
7.8%
경 5948
 
7.8%
시 5918
 
7.7%
동 5336
 
7.0%
구 3541
 
4.6%
지 2466
 
3.2%
원 2278
 
3.0%
번 2217
 
2.9%
수 1906
 
2.5%
Other values (412) 35040
45.7%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct5074
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.382322
Minimum36.920293
Maximum38.152322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2024-04-29T22:28:13.317300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.920293
5-th percentile37.040358
Q137.258232
median37.35078
Q337.51434
95-th percentile37.751622
Maximum38.152322
Range1.2320288
Interquartile range (IQR)0.25610787

Descriptive statistics

Standard deviation0.22056454
Coefficient of variation (CV)0.0059002366
Kurtosis-0.10592517
Mean37.382322
Median Absolute Deviation (MAD)0.12722731
Skewness0.3628449
Sum221789.31
Variance0.048648716
MonotonicityNot monotonic
2024-04-29T22:28:13.450798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2863343 15
 
0.3%
37.3065417 12
 
0.2%
37.46651221 11
 
0.2%
37.2529817 11
 
0.2%
37.3001769611 11
 
0.2%
37.4645248861 10
 
0.2%
37.35409297 9
 
0.2%
37.2874698 9
 
0.2%
37.44312926 8
 
0.1%
37.3084891 8
 
0.1%
Other values (5064) 5829
98.2%
ValueCountFrequency (%)
36.920293 1
< 0.1%
36.920297 1
< 0.1%
36.920842 1
< 0.1%
36.921492 1
< 0.1%
36.921607 1
< 0.1%
36.922208 1
< 0.1%
36.932443 1
< 0.1%
36.932983 1
< 0.1%
36.934571 1
< 0.1%
36.934876 1
< 0.1%
ValueCountFrequency (%)
38.1523217635 1
< 0.1%
38.1479873949 1
< 0.1%
38.14629821 1
< 0.1%
38.1105107311 1
< 0.1%
38.1095154837 1
< 0.1%
38.1093987682 1
< 0.1%
38.1026383662 1
< 0.1%
38.1022385122 1
< 0.1%
38.0800549332 1
< 0.1%
38.0790584307 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct5072
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.96822
Minimum126.37605
Maximum127.576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2024-04-29T22:28:13.598833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.37605
5-th percentile126.76549
Q1126.85584
median126.98383
Q3127.05561
95-th percentile127.17904
Maximum127.576
Range1.199954
Interquartile range (IQR)0.1997679

Descriptive statistics

Standard deviation0.13978195
Coefficient of variation (CV)0.0011009208
Kurtosis0.69981139
Mean126.96822
Median Absolute Deviation (MAD)0.081477
Skewness0.15322024
Sum753302.44
Variance0.019538995
MonotonicityNot monotonic
2024-04-29T22:28:13.745926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0774619 15
 
0.3%
127.0417606 12
 
0.2%
126.8308011531 11
 
0.2%
127.072446 11
 
0.2%
126.8649875 11
 
0.2%
127.1593782417 10
 
0.2%
126.9310663 9
 
0.2%
127.0489718 9
 
0.2%
126.8869805 8
 
0.1%
126.8558433 8
 
0.1%
Other values (5062) 5829
98.2%
ValueCountFrequency (%)
126.3760497 1
 
< 0.1%
126.3766967 1
 
< 0.1%
126.5511470182 1
 
< 0.1%
126.5532336536 1
 
< 0.1%
126.554524206 1
 
< 0.1%
126.5560106134 1
 
< 0.1%
126.5588160558 1
 
< 0.1%
126.5647301924 1
 
< 0.1%
126.5655449651 3
0.1%
126.5714613922 6
0.1%
ValueCountFrequency (%)
127.5760037 1
< 0.1%
127.539582 1
< 0.1%
127.536323 1
< 0.1%
127.529049 1
< 0.1%
127.526001 1
< 0.1%
127.520096 1
< 0.1%
127.519577 1
< 0.1%
127.516445 1
< 0.1%
127.511695 1
< 0.1%
127.511592 1
< 0.1%

제설함수(개)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct31
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3261419
Minimum1
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2024-04-29T22:28:13.860577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum150
Range149
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7857864
Coefficient of variation (CV)2.1006699
Kurtosis1412.6498
Mean1.3261419
Median Absolute Deviation (MAD)0
Skewness29.90308
Sum7868
Variance7.7606061
MonotonicityNot monotonic
2024-04-29T22:28:13.988596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 5592
94.3%
2 110
 
1.9%
4 45
 
0.8%
3 44
 
0.7%
6 25
 
0.4%
8 23
 
0.4%
5 19
 
0.3%
7 14
 
0.2%
10 14
 
0.2%
20 6
 
0.1%
Other values (21) 41
 
0.7%
ValueCountFrequency (%)
1 5592
94.3%
2 110
 
1.9%
3 44
 
0.7%
4 45
 
0.8%
5 19
 
0.3%
6 25
 
0.4%
7 14
 
0.2%
8 23
 
0.4%
9 1
 
< 0.1%
10 14
 
0.2%
ValueCountFrequency (%)
150 1
 
< 0.1%
49 1
 
< 0.1%
44 1
 
< 0.1%
34 1
 
< 0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
30 1
 
< 0.1%
26 2
< 0.1%
25 3
0.1%
24 3
0.1%

염화칼슘비치량
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct34
Distinct (%)1.5%
Missing3633
Missing (%)61.2%
Infinite0
Infinite (%)0.0%
Mean14.911739
Minimum0
Maximum270
Zeros252
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2024-04-29T22:28:14.116165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q310
95-th percentile45
Maximum270
Range270
Interquartile range (IQR)7

Descriptive statistics

Standard deviation25.546749
Coefficient of variation (CV)1.7131972
Kurtosis18.891976
Mean14.911739
Median Absolute Deviation (MAD)3
Skewness3.686053
Sum34297
Variance652.6364
MonotonicityNot monotonic
2024-04-29T22:28:14.258838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
45 354
 
6.0%
3 337
 
5.7%
5 336
 
5.7%
6 303
 
5.1%
0 252
 
4.2%
10 198
 
3.3%
4 181
 
3.1%
8 163
 
2.7%
125 56
 
0.9%
9 52
 
0.9%
Other values (24) 68
 
1.1%
(Missing) 3633
61.2%
ValueCountFrequency (%)
0 252
4.2%
1 9
 
0.2%
2 28
 
0.5%
3 337
5.7%
4 181
3.1%
5 336
5.7%
6 303
5.1%
7 1
 
< 0.1%
8 163
2.7%
9 52
 
0.9%
ValueCountFrequency (%)
270 1
 
< 0.1%
261 1
 
< 0.1%
225 1
 
< 0.1%
207 1
 
< 0.1%
198 1
 
< 0.1%
135 1
 
< 0.1%
126 1
 
< 0.1%
125 56
0.9%
99 1
 
< 0.1%
90 2
 
< 0.1%

소금비치량
Real number (ℝ)

MISSING  ZEROS 

Distinct18
Distinct (%)1.2%
Missing4369
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean4.2007673
Minimum0
Maximum820
Zeros1533
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2024-04-29T22:28:14.373701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum820
Range820
Interquartile range (IQR)0

Descriptive statistics

Standard deviation37.507951
Coefficient of variation (CV)8.9288335
Kurtosis195.65536
Mean4.2007673
Median Absolute Deviation (MAD)0
Skewness12.423648
Sum6570
Variance1406.8464
MonotonicityNot monotonic
2024-04-29T22:28:14.475056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 1533
 
25.8%
300 4
 
0.1%
160 4
 
0.1%
60 3
 
0.1%
200 2
 
< 0.1%
20 2
 
< 0.1%
80 2
 
< 0.1%
140 2
 
< 0.1%
100 2
 
< 0.1%
420 2
 
< 0.1%
Other values (8) 8
 
0.1%
(Missing) 4369
73.6%
ValueCountFrequency (%)
0 1533
25.8%
20 2
 
< 0.1%
40 1
 
< 0.1%
60 3
 
0.1%
80 2
 
< 0.1%
100 2
 
< 0.1%
140 2
 
< 0.1%
160 4
 
0.1%
180 1
 
< 0.1%
200 2
 
< 0.1%
ValueCountFrequency (%)
820 1
 
< 0.1%
450 1
 
< 0.1%
420 2
< 0.1%
380 1
 
< 0.1%
300 4
0.1%
280 1
 
< 0.1%
260 1
 
< 0.1%
220 1
 
< 0.1%
200 2
< 0.1%
180 1
 
< 0.1%

모래비치량
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct59
Distinct (%)2.6%
Missing3637
Missing (%)61.3%
Infinite0
Infinite (%)0.0%
Mean14.605836
Minimum0
Maximum910
Zeros949
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2024-04-29T22:28:14.613286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q325
95-th percentile30
Maximum910
Range910
Interquartile range (IQR)25

Descriptive statistics

Standard deviation40.821084
Coefficient of variation (CV)2.7948474
Kurtosis305.84286
Mean14.605836
Median Absolute Deviation (MAD)6
Skewness15.516907
Sum33535
Variance1666.3609
MonotonicityNot monotonic
2024-04-29T22:28:14.752638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 949
 
16.0%
25 261
 
4.4%
30 196
 
3.3%
20 188
 
3.2%
6 140
 
2.4%
3 140
 
2.4%
7 100
 
1.7%
19 64
 
1.1%
29 58
 
1.0%
55 28
 
0.5%
Other values (49) 172
 
2.9%
(Missing) 3637
61.3%
ValueCountFrequency (%)
0 949
16.0%
3 140
 
2.4%
4 5
 
0.1%
5 27
 
0.5%
6 140
 
2.4%
7 100
 
1.7%
8 2
 
< 0.1%
9 14
 
0.2%
10 2
 
< 0.1%
12 10
 
0.2%
ValueCountFrequency (%)
910 2
< 0.1%
840 1
< 0.1%
665 1
< 0.1%
299 1
< 0.1%
286 1
< 0.1%
280 2
< 0.1%
260 1
< 0.1%
247 1
< 0.1%
210 1
< 0.1%
195 1
< 0.1%

관리기관명
Text

Distinct85
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size46.5 KiB
2024-04-29T22:28:14.999298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length8.359346
Min length3

Characters and Unicode

Total characters49596
Distinct characters112
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

Unique3 ?
Unique (%)0.1%

Sample

1st row가평군
2nd row가평군
3rd row가평군
4th row가평군
5th row가평군
ValueCountFrequency (%)
건설과 1563
 
13.4%
건설안전과 839
 
7.2%
송탄출장소 599
 
5.1%
건설도시과 599
 
5.1%
경기도 546
 
4.7%
영통구 507
 
4.4%
권선구 405
 
3.5%
도로과 392
 
3.4%
장안구 356
 
3.1%
소사구 353
 
3.0%
Other values (87) 5486
47.1%
2024-04-29T22:28:15.390695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5712
 
11.5%
과 4028
 
8.1%
구 3455
 
7.0%
설 3355
 
6.8%
건 3341
 
6.7%
시 2703
 
5.5%
안 2199
 
4.4%
청 1946
 
3.9%
도 1734
 
3.5%
소 1372
 
2.8%
Other values (102) 19751
39.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43641
88.0%
Space Separator 5712
 
11.5%
Decimal Number 243
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 4028
 
9.2%
구 3455
 
7.9%
설 3355
 
7.7%
건 3341
 
7.7%
시 2703
 
6.2%
안 2199
 
5.0%
청 1946
 
4.5%
도 1734
 
4.0%
소 1372
 
3.1%
장 1354
 
3.1%
Other values (95) 18154
41.6%
Decimal Number
ValueCountFrequency (%)
1 117
48.1%
2 77
31.7%
3 43
 
17.7%
4 3
 
1.2%
8 2
 
0.8%
6 1
 
0.4%
Space Separator
ValueCountFrequency (%)
5712
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43641
88.0%
Common 5955
 
12.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 4028
 
9.2%
구 3455
 
7.9%
설 3355
 
7.7%
건 3341
 
7.7%
시 2703
 
6.2%
안 2199
 
5.0%
청 1946
 
4.5%
도 1734
 
4.0%
소 1372
 
3.1%
장 1354
 
3.1%
Other values (95) 18154
41.6%
Common
ValueCountFrequency (%)
5712
95.9%
1 117
 
2.0%
2 77
 
1.3%
3 43
 
0.7%
4 3
 
0.1%
8 2
 
< 0.1%
6 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43641
88.0%
ASCII 5955
 
12.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5712
95.9%
1 117
 
2.0%
2 77
 
1.3%
3 43
 
0.7%
4 3
 
0.1%
8 2
 
< 0.1%
6 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
과 4028
 
9.2%
구 3455
 
7.9%
설 3355
 
7.7%
건 3341
 
7.7%
시 2703
 
6.2%
안 2199
 
5.0%
청 1946
 
4.5%
도 1734
 
4.0%
소 1372
 
3.1%
장 1354
 
3.1%
Other values (95) 18154
41.6%

관리기관전화번호
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size46.5 KiB
<NA>
2051 
031-8024-6834
599 
032-625-6403
353 
031-8024-8432
303 
032-625-7401
295 
Other values (29)
2332 

Length

Max length13
Median length12
Mean length9.4849149
Min length4

Unique

Unique4 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2051
34.6%
031-8024-6834 599
 
10.1%
032-625-6403 353
 
5.9%
031-8024-8432 303
 
5.1%
032-625-7401 295
 
5.0%
031-590-4372 260
 
4.4%
031-390-0526 234
 
3.9%
032-625-5412 191
 
3.2%
031-8075-7314 181
 
3.1%
02-2680-6208 152
 
2.6%
Other values (24) 1314
22.1%

Length

2024-04-29T22:28:15.521053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 2051
34.6%
031-8024-6834 599
 
10.1%
032-625-6403 353
 
5.9%
031-8024-8432 303
 
5.1%
032-625-7401 295
 
5.0%
031-590-4372 260
 
4.4%
031-390-0526 234
 
3.9%
032-625-5412 191
 
3.2%
031-8075-7314 181
 
3.1%
02-2680-6208 152
 
2.6%
Other values (24) 1314
22.1%

데이터기준일자
Date

Distinct23
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size46.5 KiB
Minimum2020-01-02 00:00:00
Maximum2024-04-23 00:00:00
2024-04-29T22:28:15.872698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:15.987289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)

Interactions

2024-04-29T22:28:08.222088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:05.373533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:05.981419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:06.533245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:07.091678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:07.687805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:08.317998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:05.528450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:06.078957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:06.629979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:07.192416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:07.781163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:08.419571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:05.624515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:06.181702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:06.730713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:07.286357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:07.870851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:08.510383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:05.722556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:06.270595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:06.825496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:07.405633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:07.954959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:08.607216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:05.812002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:06.366563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:06.928581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:07.499844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:08.050946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:08.706352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:05.894243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:06.449471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:07.011758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:07.601885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:28:08.130023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-29T22:28:16.090600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명위도경도제설함수(개)염화칼슘비치량소금비치량모래비치량관리기관명관리기관전화번호데이터기준일자
시군명1.0000.9530.9300.8290.6930.5640.7951.0001.0001.000
위도0.9531.0000.7840.1060.4920.6480.2430.9820.9670.944
경도0.9300.7841.0000.5160.4480.5850.6900.9570.9390.915
제설함수(개)0.8290.1060.5161.0000.9650.7670.9770.8660.8630.819
염화칼슘비치량0.6930.4920.4480.9651.000NaN0.9730.8460.8380.693
소금비치량0.5640.6480.5850.767NaN1.000NaN0.5830.5830.564
모래비치량0.7950.2430.6900.9770.973NaN1.0000.6990.7140.795
관리기관명1.0000.9820.9570.8660.8460.5830.6991.0001.0001.000
관리기관전화번호1.0000.9670.9390.8630.8380.5830.7141.0001.0001.000
데이터기준일자1.0000.9440.9150.8190.6930.5640.7951.0001.0001.000
2024-04-29T22:28:16.245982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리기관전화번호시군명
관리기관전화번호1.0000.999
시군명0.9991.000
2024-04-29T22:28:16.350400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도제설함수(개)염화칼슘비치량소금비치량모래비치량시군명관리기관전화번호
위도1.000-0.2100.184-0.6470.237-0.2710.7600.801
경도-0.2101.0000.047-0.3640.1100.7740.6860.699
제설함수(개)0.1840.0471.000-0.0340.4850.1340.5770.642
염화칼슘비치량-0.647-0.364-0.0341.000NaN-0.7310.3790.539
소금비치량0.2370.1100.485NaN1.000NaN0.3450.343
모래비치량-0.2710.7740.134-0.731NaN1.0000.4140.411
시군명0.7600.6860.5770.3790.3450.4141.0000.999
관리기관전화번호0.8010.6990.6420.5390.3430.4110.9991.000

Missing values

2024-04-29T22:28:09.017099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-29T22:28:09.199054image/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.
2024-04-29T22:28:09.364028image/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

시군명읍면동명관리번호설치장소명소재지도로명주소소재지지번주소위도경도제설함수(개)염화칼슘비치량소금비치량모래비치량관리기관명관리기관전화번호데이터기준일자
0가평군청평면17고성리경기도 가평군 청평면 말래골길 6경기도 가평군 청평면 고성리 437-1번지37.715821127.50142720<NA><NA><NA>가평군<NA>2023-08-07
1가평군가평읍10금대리경기도 가평군 가평읍 북한강변로 320-16경기도 가평군 가평읍 금대리 140번지37.756739127.5363231<NA><NA><NA>가평군<NA>2023-08-07
2가평군가평읍8달전2리경기도 가평군 가평읍 강산길 14경기도 가평군 가평읍 달전리 454번지37.814086127.5164452<NA><NA><NA>가평군<NA>2023-08-07
3가평군가평읍5달전리경기도 가평군 가평읍 북한강변로 1127-1경기도 가평군 가평읍 달전리 312-1번지37.811236127.5200966<NA><NA><NA>가평군<NA>2023-08-07
4가평군가평읍7두밀리경기도 가평군 가평읍 태봉두밀로 422-5경기도 가평군 가평읍 두밀리 44-6번지37.81636127.4560387<NA><NA><NA>가평군<NA>2023-08-07
5가평군설악면14미사리경기도 가평군 설악면 미사리로 633경기도 가평군 설악면 미사리 308-3번지37.707531127.53958232<NA><NA><NA>가평군<NA>2023-08-07
6가평군가평읍9복장리경기도 가평군 가평읍 상지로 1098경기도 가평군 가평읍 복장리 345-2번지37.736666127.50359431<NA><NA><NA>가평군<NA>2023-08-07
7가평군청평면15사룡리경기도 가평군 설악면 자잠로 198경기도 가평군 설악면 사룡리 산38-1번지37.692186127.4879246<NA><NA><NA>가평군<NA>2023-08-07
8가평군가평읍2산유리경기도 가평군 가평읍 호반로 1827경기도 가평군 가평읍 산유리 209번지37.756724127.51169525<NA><NA><NA>가평군<NA>2023-08-07
9가평군청평면18삼회리경기도 가평군 청평면 북한강로 1633경기도 가평군 청평면 삼회리 475-6번지37.667442127.3807420<NA><NA><NA>가평군<NA>2023-08-07
시군명읍면동명관리번호설치장소명소재지도로명주소소재지지번주소위도경도제설함수(개)염화칼슘비치량소금비치량모래비치량관리기관명관리기관전화번호데이터기준일자
5923화성시<NA><NA><NA><NA>경기도 화성시 기안동 산16-2237.225461126.9811811<NA><NA><NA>화성시 동부출장소<NA>2023-02-15
5924화성시<NA><NA><NA>경기도 화성시 세자로442번길 26경기도 화성시 안녕동 180-46237.204289126.9857771<NA><NA><NA>화성시 동부출장소<NA>2023-02-15
5925화성시<NA><NA><NA>경기도 화성시 안녕남로96번길 33경기도 화성시 안녕동 178-17037.194979126.9944711<NA><NA><NA>화성시 동부출장소<NA>2023-02-15
5926화성시<NA><NA><NA>경기도 화성시 안녕남로8번길 15경기도 화성시 안녕동 183-1337.19701126.9847561<NA><NA><NA>화성시 동부출장소<NA>2023-02-15
5927화성시<NA><NA><NA>경기도 화성시 안녕북길 39-17경기도 화성시 안녕동 180-8637.206937126.9812061<NA><NA><NA>화성시 동부출장소<NA>2023-02-15
5928화성시<NA><NA><NA><NA>경기도 화성시 송산동 100-5637.206414127.0158141<NA><NA><NA>화성시 동부출장소<NA>2023-02-15
5929화성시<NA><NA><NA><NA>경기도 화성시 송산동 100-2037.207522127.0170871<NA><NA><NA>화성시 동부출장소<NA>2023-02-15
5930화성시<NA><NA><NA><NA>경기도 화성시 송산동 148-1937.209414127.0150371<NA><NA><NA>화성시 동부출장소<NA>2023-02-15
5931화성시<NA><NA><NA><NA>경기도 화성시 정남면 음양리 52237.149087127.029651<NA><NA><NA>화성시 정남면<NA>2023-02-15
5932화성시<NA><NA><NA><NA>경기도 화성시 정남면 음양리 590-237.141699127.0277341<NA><NA><NA>화성시 정남면<NA>2023-02-15

Duplicate rows

Most frequently occurring

시군명읍면동명관리번호설치장소명소재지도로명주소소재지지번주소위도경도제설함수(개)염화칼슘비치량소금비치량모래비치량관리기관명관리기관전화번호데이터기준일자# duplicates
0성남시<NA><NA>육교 밑<NA>경기도 성남시 수정구 창곡동 산11937.458554127.150291<NA><NA><NA>수정구 건설과<NA>2022-06-082
1화성시<NA><NA><NA><NA>경기도 화성시 반월동 184-737.232244127.0561721<NA><NA><NA>화성시 동부출장소<NA>2023-02-152
2화성시<NA><NA><NA><NA>경기도 화성시 반월동 535-937.223718127.0607741<NA><NA><NA>화성시 동부출장소<NA>2023-02-152
3화성시<NA><NA><NA><NA>경기도 화성시 병점동 849-237.212083127.0475841<NA><NA><NA>화성시 동부출장소<NA>2023-02-152