Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells10192
Missing cells (%)17.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory585.9 KiB
Average record size in memory60.0 B

Variable types

Numeric4
Text2

Dataset

Description지역의 급경사지 위치 및 위험성 등 부분적 정보제공
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=3026

Alerts

지역코드 is highly overall correlated with 일련번호High correlation
일련번호 is highly overall correlated with 지역코드High correlation
비탈면용도(보호목적)코드 has 1892 (18.9%) missing valuesMissing
주소 has 8300 (83.0%) missing valuesMissing
일련번호 has unique valuesUnique

Reproduction

Analysis started2024-01-09 20:43:51.910964
Analysis finished2024-01-09 20:43:54.666048
Duration2.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct4734
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2939062 × 109
Minimum1.1110101 × 109
Maximum5.183035 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:43:54.735946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110101 × 109
5-th percentile1.1680113 × 109
Q14.182031 × 109
median4.678031 × 109
Q34.8330253 × 109
95-th percentile5.1770259 × 109
Maximum5.183035 × 109
Range4.0720249 × 109
Interquartile range (IQR)6.509943 × 108

Descriptive statistics

Standard deviation9.9335199 × 108
Coefficient of variation (CV)0.23133994
Kurtosis3.0472728
Mean4.2939062 × 109
Median Absolute Deviation (MAD)3.00994 × 108
Skewness-1.9112802
Sum4.2939062 × 1013
Variance9.8674818 × 1017
MonotonicityNot monotonic
2024-01-10T05:43:54.867603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5181032024 37
 
0.4%
5121011200 31
 
0.3%
5179025027 29
 
0.3%
1154510300 26
 
0.3%
4825036022 21
 
0.2%
5177025321 19
 
0.2%
5176036027 19
 
0.2%
4777025028 18
 
0.2%
1126010100 18
 
0.2%
4182032522 17
 
0.2%
Other values (4724) 9765
97.7%
ValueCountFrequency (%)
1111010100 1
 
< 0.1%
1111011100 1
 
< 0.1%
1111011500 1
 
< 0.1%
1111017000 1
 
< 0.1%
1111017400 5
0.1%
1111017800 3
< 0.1%
1111018200 3
< 0.1%
1111018300 5
0.1%
1111018400 2
 
< 0.1%
1111018700 1
 
< 0.1%
ValueCountFrequency (%)
5183035042 1
 
< 0.1%
5183035036 1
 
< 0.1%
5183035031 3
< 0.1%
5183034038 2
< 0.1%
5183034031 1
 
< 0.1%
5183034030 2
< 0.1%
5183034023 1
 
< 0.1%
5183034021 2
< 0.1%
5183033032 1
 
< 0.1%
5183033030 2
< 0.1%

비탈면용도(보호목적)코드
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)0.1%
Missing1892
Missing (%)18.9%
Infinite0
Infinite (%)0.0%
Mean2.1546621
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:43:54.978613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7194371
Coefficient of variation (CV)0.7980078
Kurtosis0.13215627
Mean2.1546621
Median Absolute Deviation (MAD)0
Skewness1.2504226
Sum17470
Variance2.956464
MonotonicityNot monotonic
2024-01-10T05:43:55.079476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 4978
49.8%
3 1236
 
12.4%
6 831
 
8.3%
2 445
 
4.5%
5 436
 
4.4%
4 182
 
1.8%
(Missing) 1892
 
18.9%
ValueCountFrequency (%)
1 4978
49.8%
2 445
 
4.5%
3 1236
 
12.4%
4 182
 
1.8%
5 436
 
4.4%
6 831
 
8.3%
ValueCountFrequency (%)
6 831
 
8.3%
5 436
 
4.4%
4 182
 
1.8%
3 1236
 
12.4%
2 445
 
4.5%
1 4978
49.8%

급경사지명
Text

Distinct9627
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T05:43:55.400688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length8.7954
Min length1

Characters and Unicode

Total characters87954
Distinct characters552
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9327 ?
Unique (%)93.3%

Sample

1st row춘의동 48-15 우
2nd row강원-영동선065-경
3rd row경기 포천 내촌 음현 N4지구
4th row창원대로내동공원
5th row충청 경부고속28
ValueCountFrequency (%)
n1지구 671
 
3.3%
n2지구 366
 
1.8%
경기 313
 
1.5%
강원 268
 
1.3%
경북 262
 
1.3%
충청 247
 
1.2%
경남 245
 
1.2%
n3지구 189
 
0.9%
전남 172
 
0.9%
충북 160
 
0.8%
Other values (9882) 17338
85.7%
2024-01-10T05:43:55.856098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10231
 
11.6%
지 5833
 
6.6%
구 5352
 
6.1%
1 4130
 
4.7%
2 2575
 
2.9%
- 2181
 
2.5%
N 1854
 
2.1%
0 1822
 
2.1%
동 1770
 
2.0%
경 1744
 
2.0%
Other values (542) 50462
57.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 55355
62.9%
Decimal Number 15816
 
18.0%
Space Separator 10231
 
11.6%
Dash Punctuation 2181
 
2.5%
Uppercase Letter 1941
 
2.2%
Open Punctuation 1101
 
1.3%
Close Punctuation 1097
 
1.2%
Math Symbol 80
 
0.1%
Lowercase Letter 80
 
0.1%
Other Punctuation 54
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 5833
 
10.5%
구 5352
 
9.7%
동 1770
 
3.2%
경 1744
 
3.2%
산 1383
 
2.5%
남 1138
 
2.1%
선 1017
 
1.8%
원 981
 
1.8%
북 935
 
1.7%
천 839
 
1.5%
Other values (489) 34363
62.1%
Uppercase Letter
ValueCountFrequency (%)
N 1854
95.5%
A 21
 
1.1%
S 9
 
0.5%
M 7
 
0.4%
K 6
 
0.3%
H 5
 
0.3%
C 5
 
0.3%
I 4
 
0.2%
B 4
 
0.2%
P 4
 
0.2%
Other values (9) 22
 
1.1%
Decimal Number
ValueCountFrequency (%)
1 4130
26.1%
2 2575
16.3%
0 1822
11.5%
3 1633
 
10.3%
4 1483
 
9.4%
5 1052
 
6.7%
6 948
 
6.0%
7 821
 
5.2%
8 764
 
4.8%
9 588
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
k 36
45.0%
t 10
 
12.5%
m 7
 
8.8%
e 7
 
8.8%
s 7
 
8.8%
d 4
 
5.0%
f 4
 
5.0%
z 2
 
2.5%
a 2
 
2.5%
p 1
 
1.2%
Other Punctuation
ValueCountFrequency (%)
& 12
22.2%
# 12
22.2%
; 12
22.2%
. 8
14.8%
@ 7
13.0%
· 1
 
1.9%
/ 1
 
1.9%
! 1
 
1.9%
Space Separator
ValueCountFrequency (%)
10231
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2181
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1101
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1097
100.0%
Math Symbol
ValueCountFrequency (%)
~ 80
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 55355
62.9%
Common 30578
34.8%
Latin 2021
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 5833
 
10.5%
구 5352
 
9.7%
동 1770
 
3.2%
경 1744
 
3.2%
산 1383
 
2.5%
남 1138
 
2.1%
선 1017
 
1.8%
원 981
 
1.8%
북 935
 
1.7%
천 839
 
1.5%
Other values (489) 34363
62.1%
Latin
ValueCountFrequency (%)
N 1854
91.7%
k 36
 
1.8%
A 21
 
1.0%
t 10
 
0.5%
S 9
 
0.4%
m 7
 
0.3%
e 7
 
0.3%
M 7
 
0.3%
s 7
 
0.3%
K 6
 
0.3%
Other values (19) 57
 
2.8%
Common
ValueCountFrequency (%)
10231
33.5%
1 4130
13.5%
2 2575
 
8.4%
- 2181
 
7.1%
0 1822
 
6.0%
3 1633
 
5.3%
4 1483
 
4.8%
( 1101
 
3.6%
) 1097
 
3.6%
5 1052
 
3.4%
Other values (14) 3273
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 55355
62.9%
ASCII 32598
37.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10231
31.4%
1 4130
12.7%
2 2575
 
7.9%
- 2181
 
6.7%
N 1854
 
5.7%
0 1822
 
5.6%
3 1633
 
5.0%
4 1483
 
4.5%
( 1101
 
3.4%
) 1097
 
3.4%
Other values (42) 4491
13.8%
Hangul
ValueCountFrequency (%)
지 5833
 
10.5%
구 5352
 
9.7%
동 1770
 
3.2%
경 1744
 
3.2%
산 1383
 
2.5%
남 1138
 
2.1%
선 1017
 
1.8%
원 981
 
1.8%
북 935
 
1.7%
천 839
 
1.5%
Other values (489) 34363
62.1%
None
ValueCountFrequency (%)
· 1
100.0%

관리주체구분
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3888
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:43:55.965412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q38
95-th percentile9
Maximum99
Range98
Interquartile range (IQR)7

Descriptive statistics

Standard deviation13.180713
Coefficient of variation (CV)2.4459459
Kurtosis43.852747
Mean5.3888
Median Absolute Deviation (MAD)0
Skewness6.5765305
Sum53888
Variance173.73121
MonotonicityNot monotonic
2024-01-10T05:43:56.057312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 5188
51.9%
8 1798
 
18.0%
5 1405
 
14.1%
9 590
 
5.9%
7 331
 
3.3%
3 232
 
2.3%
2 197
 
2.0%
99 184
 
1.8%
4 54
 
0.5%
6 17
 
0.2%
ValueCountFrequency (%)
1 5188
51.9%
2 197
 
2.0%
3 232
 
2.3%
4 54
 
0.5%
5 1405
 
14.1%
6 17
 
0.2%
7 331
 
3.3%
8 1798
 
18.0%
9 590
 
5.9%
10 4
 
< 0.1%
ValueCountFrequency (%)
99 184
 
1.8%
10 4
 
< 0.1%
9 590
 
5.9%
8 1798
18.0%
7 331
 
3.3%
6 17
 
0.2%
5 1405
14.1%
4 54
 
0.5%
3 232
 
2.3%
2 197
 
2.0%

주소
Text

MISSING 

Distinct1407
Distinct (%)82.8%
Missing8300
Missing (%)83.0%
Memory size156.2 KiB
2024-01-10T05:43:56.292665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length39
Mean length12.174706
Min length1

Characters and Unicode

Total characters20697
Distinct characters485
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1302 ?
Unique (%)76.6%

Sample

1st row영동선 녹동-임기간 46k210 (좌)
2nd row91km170~91km400(상우)
3rd row소입현마을 뒤
4th row대근연립 옹벽
5th row광명~천안아산(상)82.230~82.390
ValueCountFrequency (%)
일원 65
 
1.8%
좌 65
 
1.8%
영동선 62
 
1.7%
우 51
 
1.4%
뒤 44
 
1.2%
중앙선 42
 
1.1%
강릉선 40
 
1.1%
서원기(현 31
 
0.8%
태백선 30
 
0.8%
지방도 28
 
0.8%
Other values (2125) 3196
87.5%
2024-01-10T05:43:56.722670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1954
 
9.4%
0 921
 
4.4%
1 792
 
3.8%
) 615
 
3.0%
( 614
 
3.0%
선 596
 
2.9%
2 538
 
2.6%
4 502
 
2.4%
- 486
 
2.3%
5 408
 
2.0%
Other values (475) 13271
64.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11346
54.8%
Decimal Number 4592
22.2%
Space Separator 1954
 
9.4%
Close Punctuation 615
 
3.0%
Open Punctuation 615
 
3.0%
Lowercase Letter 575
 
2.8%
Dash Punctuation 486
 
2.3%
Other Punctuation 279
 
1.3%
Math Symbol 190
 
0.9%
Uppercase Letter 45
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
선 596
 
5.3%
도 384
 
3.4%
지 319
 
2.8%
동 307
 
2.7%
산 295
 
2.6%
원 260
 
2.3%
호 242
 
2.1%
간 231
 
2.0%
리 189
 
1.7%
아 185
 
1.6%
Other values (429) 8338
73.5%
Uppercase Letter
ValueCountFrequency (%)
K 16
35.6%
C 6
 
13.3%
L 4
 
8.9%
I 4
 
8.9%
E 2
 
4.4%
V 2
 
4.4%
S 2
 
4.4%
W 2
 
4.4%
H 2
 
4.4%
R 1
 
2.2%
Other values (4) 4
 
8.9%
Decimal Number
ValueCountFrequency (%)
0 921
20.1%
1 792
17.2%
2 538
11.7%
4 502
10.9%
5 408
8.9%
3 358
 
7.8%
8 297
 
6.5%
6 283
 
6.2%
7 263
 
5.7%
9 230
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
k 393
68.3%
m 174
30.3%
t 2
 
0.3%
e 2
 
0.3%
s 1
 
0.2%
x 1
 
0.2%
n 1
 
0.2%
d 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 104
37.3%
; 56
20.1%
# 56
20.1%
& 56
20.1%
/ 5
 
1.8%
: 2
 
0.7%
Math Symbol
ValueCountFrequency (%)
~ 187
98.4%
= 2
 
1.1%
+ 1
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 614
99.8%
[ 1
 
0.2%
Space Separator
ValueCountFrequency (%)
1954
100.0%
Close Punctuation
ValueCountFrequency (%)
) 615
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 486
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11346
54.8%
Common 8731
42.2%
Latin 620
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
선 596
 
5.3%
도 384
 
3.4%
지 319
 
2.8%
동 307
 
2.7%
산 295
 
2.6%
원 260
 
2.3%
호 242
 
2.1%
간 231
 
2.0%
리 189
 
1.7%
아 185
 
1.6%
Other values (429) 8338
73.5%
Common
ValueCountFrequency (%)
1954
22.4%
0 921
10.5%
1 792
9.1%
) 615
 
7.0%
( 614
 
7.0%
2 538
 
6.2%
4 502
 
5.7%
- 486
 
5.6%
5 408
 
4.7%
3 358
 
4.1%
Other values (14) 1543
17.7%
Latin
ValueCountFrequency (%)
k 393
63.4%
m 174
28.1%
K 16
 
2.6%
C 6
 
1.0%
L 4
 
0.6%
I 4
 
0.6%
E 2
 
0.3%
V 2
 
0.3%
S 2
 
0.3%
W 2
 
0.3%
Other values (12) 15
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11346
54.8%
ASCII 9351
45.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1954
20.9%
0 921
9.8%
1 792
 
8.5%
) 615
 
6.6%
( 614
 
6.6%
2 538
 
5.8%
4 502
 
5.4%
- 486
 
5.2%
5 408
 
4.4%
k 393
 
4.2%
Other values (36) 2128
22.8%
Hangul
ValueCountFrequency (%)
선 596
 
5.3%
도 384
 
3.4%
지 319
 
2.8%
동 307
 
2.7%
산 295
 
2.6%
원 260
 
2.3%
호 242
 
2.1%
간 231
 
2.0%
리 189
 
1.7%
아 185
 
1.6%
Other values (429) 8338
73.5%

일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1604663 × 109
Minimum1.111 × 109
Maximum5.18 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:43:56.853316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111 × 109
5-th percentile1.1680001 × 109
Q14.1820002 × 109
median4.3800002 × 109
Q34.7250002 × 109
95-th percentile4.8850001 × 109
Maximum5.18 × 109
Range4.069 × 109
Interquartile range (IQR)5.4299999 × 108

Descriptive statistics

Standard deviation9.2321868 × 108
Coefficient of variation (CV)0.22190269
Kurtosis3.6596403
Mean4.1604663 × 109
Median Absolute Deviation (MAD)3.0199991 × 108
Skewness-2.0615537
Sum4.1604663 × 1013
Variance8.5233273 × 1017
MonotonicityNot monotonic
2024-01-10T05:43:56.989660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4119500004 1
 
< 0.1%
4413100020 1
 
< 0.1%
4155000314 1
 
< 0.1%
4427000046 1
 
< 0.1%
2623000026 1
 
< 0.1%
4882000184 1
 
< 0.1%
4418000109 1
 
< 0.1%
4885000330 1
 
< 0.1%
4577000025 1
 
< 0.1%
4679000010 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1111000028 1
< 0.1%
1111000030 1
< 0.1%
1111000031 1
< 0.1%
1111000032 1
< 0.1%
1111000035 1
< 0.1%
1111000039 1
< 0.1%
1111000042 1
< 0.1%
1111000043 1
< 0.1%
1111000044 1
< 0.1%
1111000045 1
< 0.1%
ValueCountFrequency (%)
5180000018 1
< 0.1%
5180000017 1
< 0.1%
5180000013 1
< 0.1%
5180000012 1
< 0.1%
5180000011 1
< 0.1%
5180000010 1
< 0.1%
5180000009 1
< 0.1%
5180000007 1
< 0.1%
5180000003 1
< 0.1%
5177000012 1
< 0.1%

Interactions

2024-01-10T05:43:54.026587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:52.695298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:53.317881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:53.673764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:54.126955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:53.021454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:53.416584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:53.762961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:54.216067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:53.113893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:53.501105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:53.841331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:54.308981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:53.207256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:53.581715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:43:53.928475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:43:57.102713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역코드비탈면용도(보호목적)코드관리주체구분일련번호
지역코드1.0000.3160.2010.998
비탈면용도(보호목적)코드0.3161.0000.2340.312
관리주체구분0.2010.2341.0000.205
일련번호0.9980.3120.2051.000
2024-01-10T05:43:57.208145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역코드비탈면용도(보호목적)코드관리주체구분일련번호
지역코드1.000-0.063-0.1630.603
비탈면용도(보호목적)코드-0.0631.0000.264-0.130
관리주체구분-0.1630.2641.000-0.153
일련번호0.603-0.130-0.1531.000

Missing values

2024-01-10T05:43:54.431397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:43:54.528824image/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.
2024-01-10T05:43:54.619526image/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

지역코드비탈면용도(보호목적)코드급경사지명관리주체구분주소일련번호
672441195103001춘의동 48-15 우1<NA>4119500004
247554792035023<NA>강원-영동선065-경5영동선 녹동-임기간 46k210 (좌)4792000182
1686641650320263경기 포천 내촌 음현 N4지구8<NA>4165000075
2091548123111001창원대로내동공원1<NA>4811000047
118784420035033<NA>충청 경부고속28591km170~91km400(상우)4420000132
2016548720370261지정 태부2지구1<NA>4872000054
1578851110340231춘천601<NA>4211000067
94841132010800<NA>418계단7<NA>1132000027
767943113320281자명골21<NA>4371000081
2586448840250301입현31소입현마을 뒤4884000011
지역코드비탈면용도(보호목적)코드급경사지명관리주체구분주소일련번호
1443951230320361용연리(2)8<NA>4223000326
1021745190320251수지 호곡11<NA>4519000011
219445800380223전북 부안 백산 용계 N1지구1<NA>4580000065
2184551210101001영랑51<NA>4221000011
828026290110003우암1동부산외대일원28<NA>2629000087
2436648860250306정곡지구1<NA>4886000098
478831170102001화정동 산187-21<NA>3117000005
1751246770310253고옥11<NA>4677000154
2537948250129001장유57지구1(불모산 휴게소 800m전 우측사면)4825000083
858511650104001원지동 355-71<NA>1165000086