Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells9404
Missing cells (%)15.7%
Duplicate rows555
Duplicate rows (%)5.5%
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=3024

Alerts

Dataset has 555 (5.5%) duplicate rowsDuplicates
지역코드 is highly overall correlated with 일련번호High correlation
일련번호 is highly overall correlated with 지역코드High correlation
비탈면용도(보호목적)코드 has 951 (9.5%) missing valuesMissing
주소 has 8453 (84.5%) missing valuesMissing

Reproduction

Analysis started2024-01-09 21:47:55.230962
Analysis finished2024-01-09 21:47:57.753144
Duration2.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct4555
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2879289 × 109
Minimum1.1110101 × 109
Maximum5.183035 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:47:57.815609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110101 × 109
5-th percentile1.1680103 × 109
Q14.165032 × 109
median4.6710333 × 109
Q34.872031 × 109
95-th percentile5.177025 × 109
Maximum5.183035 × 109
Range4.0720249 × 109
Interquartile range (IQR)7.06999 × 108

Descriptive statistics

Standard deviation9.995312 × 108
Coefficient of variation (CV)0.23310349
Kurtosis3.0834519
Mean4.2879289 × 109
Median Absolute Deviation (MAD)2.9500026 × 108
Skewness-1.921165
Sum4.2879289 × 1013
Variance9.9906263 × 1017
MonotonicityNot monotonic
2024-01-10T06:47:57.929322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5179025027 31
 
0.3%
1154510300 31
 
0.3%
5121011200 27
 
0.3%
5177025923 26
 
0.3%
1162010200 24
 
0.2%
1129013300 22
 
0.2%
5113031024 21
 
0.2%
4673035022 20
 
0.2%
5176034025 19
 
0.2%
4673037026 18
 
0.2%
Other values (4545) 9761
97.6%
ValueCountFrequency (%)
1111010100 1
 
< 0.1%
1111010900 1
 
< 0.1%
1111011100 1
 
< 0.1%
1111016500 1
 
< 0.1%
1111017100 1
 
< 0.1%
1111017300 1
 
< 0.1%
1111017400 5
0.1%
1111017800 1
 
< 0.1%
1111018200 2
 
< 0.1%
1111018300 8
0.1%
ValueCountFrequency (%)
5183035036 1
 
< 0.1%
5183035031 4
< 0.1%
5183035025 1
 
< 0.1%
5183034038 2
< 0.1%
5183034030 2
< 0.1%
5183034022 1
 
< 0.1%
5183034021 2
< 0.1%
5183033033 1
 
< 0.1%
5183033030 1
 
< 0.1%
5183033028 1
 
< 0.1%

비탈면용도(보호목적)코드
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)0.1%
Missing951
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean2.2350536
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:47:58.024021image/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 deviation1.7574221
Coefficient of variation (CV)0.78629975
Kurtosis-0.024954607
Mean2.2350536
Median Absolute Deviation (MAD)0
Skewness1.1791709
Sum20225
Variance3.0885324
MonotonicityNot monotonic
2024-01-10T06:47:58.105085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 5320
53.2%
3 1625
 
16.2%
6 1106
 
11.1%
2 467
 
4.7%
5 336
 
3.4%
4 195
 
1.9%
(Missing) 951
 
9.5%
ValueCountFrequency (%)
1 5320
53.2%
2 467
 
4.7%
3 1625
 
16.2%
4 195
 
1.9%
5 336
 
3.4%
6 1106
 
11.1%
ValueCountFrequency (%)
6 1106
 
11.1%
5 336
 
3.4%
4 195
 
1.9%
3 1625
 
16.2%
2 467
 
4.7%
1 5320
53.2%

급경사지명
Text

Distinct9141
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T06:47:58.387865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length27
Mean length9.3858
Min length1

Characters and Unicode

Total characters93858
Distinct characters543
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8389 ?
Unique (%)83.9%

Sample

1st row용호4동목련맨션사면
2nd row서울 은평 구산 N1지구
3rd row경기 용인 수지 신봉 N3지구
4th row청도 청도 원정 N2지구
5th row만종지구
ValueCountFrequency (%)
n1지구 1044
 
4.5%
n2지구 544
 
2.3%
경기 488
 
2.1%
강원 465
 
2.0%
경북 397
 
1.7%
경남 356
 
1.5%
n3지구 290
 
1.3%
충북 202
 
0.9%
전남 191
 
0.8%
충청 185
 
0.8%
Other values (8944) 18999
82.0%
2024-01-10T06:47:58.803112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13161
 
14.0%
지 6310
 
6.7%
구 5877
 
6.3%
1 4216
 
4.5%
N 2710
 
2.9%
2 2603
 
2.8%
경 1935
 
2.1%
동 1797
 
1.9%
3 1651
 
1.8%
산 1581
 
1.7%
Other values (533) 52017
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59109
63.0%
Decimal Number 15275
 
16.3%
Space Separator 13161
 
14.0%
Uppercase Letter 2789
 
3.0%
Dash Punctuation 1520
 
1.6%
Open Punctuation 871
 
0.9%
Close Punctuation 867
 
0.9%
Lowercase Letter 106
 
0.1%
Math Symbol 101
 
0.1%
Other Punctuation 56
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 6310
 
10.7%
구 5877
 
9.9%
경 1935
 
3.3%
동 1797
 
3.0%
산 1581
 
2.7%
남 1253
 
2.1%
북 1170
 
2.0%
원 1124
 
1.9%
강 929
 
1.6%
천 912
 
1.5%
Other values (484) 36221
61.3%
Uppercase Letter
ValueCountFrequency (%)
N 2710
97.2%
A 13
 
0.5%
S 11
 
0.4%
I 9
 
0.3%
C 9
 
0.3%
K 7
 
0.3%
H 7
 
0.3%
T 5
 
0.2%
M 4
 
0.1%
D 3
 
0.1%
Other values (6) 11
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 4216
27.6%
2 2603
17.0%
3 1651
 
10.8%
4 1456
 
9.5%
0 1448
 
9.5%
5 982
 
6.4%
7 862
 
5.6%
6 858
 
5.6%
8 702
 
4.6%
9 497
 
3.3%
Lowercase Letter
ValueCountFrequency (%)
k 73
68.9%
m 15
 
14.2%
s 5
 
4.7%
e 4
 
3.8%
t 3
 
2.8%
d 2
 
1.9%
a 2
 
1.9%
f 1
 
0.9%
p 1
 
0.9%
Other Punctuation
ValueCountFrequency (%)
# 11
19.6%
& 11
19.6%
; 11
19.6%
. 10
17.9%
@ 9
16.1%
! 4
 
7.1%
Open Punctuation
ValueCountFrequency (%)
( 870
99.9%
[ 1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 866
99.9%
] 1
 
0.1%
Space Separator
ValueCountFrequency (%)
13161
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1520
100.0%
Math Symbol
ValueCountFrequency (%)
~ 101
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59109
63.0%
Common 31854
33.9%
Latin 2895
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 6310
 
10.7%
구 5877
 
9.9%
경 1935
 
3.3%
동 1797
 
3.0%
산 1581
 
2.7%
남 1253
 
2.1%
북 1170
 
2.0%
원 1124
 
1.9%
강 929
 
1.6%
천 912
 
1.5%
Other values (484) 36221
61.3%
Latin
ValueCountFrequency (%)
N 2710
93.6%
k 73
 
2.5%
m 15
 
0.5%
A 13
 
0.4%
S 11
 
0.4%
I 9
 
0.3%
C 9
 
0.3%
K 7
 
0.2%
H 7
 
0.2%
s 5
 
0.2%
Other values (15) 36
 
1.2%
Common
ValueCountFrequency (%)
13161
41.3%
1 4216
 
13.2%
2 2603
 
8.2%
3 1651
 
5.2%
- 1520
 
4.8%
4 1456
 
4.6%
0 1448
 
4.5%
5 982
 
3.1%
( 870
 
2.7%
) 866
 
2.7%
Other values (14) 3081
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59109
63.0%
ASCII 34749
37.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13161
37.9%
1 4216
 
12.1%
N 2710
 
7.8%
2 2603
 
7.5%
3 1651
 
4.8%
- 1520
 
4.4%
4 1456
 
4.2%
0 1448
 
4.2%
5 982
 
2.8%
( 870
 
2.5%
Other values (39) 4132
 
11.9%
Hangul
ValueCountFrequency (%)
지 6310
 
10.7%
구 5877
 
9.9%
경 1935
 
3.3%
동 1797
 
3.0%
산 1581
 
2.7%
남 1253
 
2.1%
북 1170
 
2.0%
원 1124
 
1.9%
강 929
 
1.6%
천 912
 
1.5%
Other values (484) 36221
61.3%

관리주체구분
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6117
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:47:58.905106image/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 deviation13.688523
Coefficient of variation (CV)2.4392827
Kurtosis40.148563
Mean5.6117
Median Absolute Deviation (MAD)0
Skewness6.3026251
Sum56117
Variance187.37566
MonotonicityNot monotonic
2024-01-10T06:47:58.994697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 5286
52.9%
8 2039
 
20.4%
5 1025
 
10.2%
9 779
 
7.8%
3 233
 
2.3%
99 199
 
2.0%
2 191
 
1.9%
7 187
 
1.9%
4 45
 
0.4%
6 12
 
0.1%
ValueCountFrequency (%)
1 5286
52.9%
2 191
 
1.9%
3 233
 
2.3%
4 45
 
0.4%
5 1025
 
10.2%
6 12
 
0.1%
7 187
 
1.9%
8 2039
 
20.4%
9 779
 
7.8%
10 4
 
< 0.1%
ValueCountFrequency (%)
99 199
 
2.0%
10 4
 
< 0.1%
9 779
 
7.8%
8 2039
20.4%
7 187
 
1.9%
6 12
 
0.1%
5 1025
10.2%
4 45
 
0.4%
3 233
 
2.3%
2 191
 
1.9%

주소
Text

MISSING 

Distinct1235
Distinct (%)79.8%
Missing8453
Missing (%)84.5%
Memory size156.2 KiB
2024-01-10T06:47:59.198393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length42
Mean length12.561732
Min length1

Characters and Unicode

Total characters19433
Distinct characters465
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1069 ?
Unique (%)69.1%

Sample

1st row부용소로 3-46
2nd row경부선 155.720-155.950(상&#44;좌)
3rd row팔야산업단지 일원
4th row하동노인전문요양원
5th row(삼가초등학교)
ValueCountFrequency (%)
강릉선 76
 
2.3%
일원 47
 
1.4%
영동선 45
 
1.4%
하선 37
 
1.1%
상선 32
 
1.0%
뒤 31
 
0.9%
우 28
 
0.9%
지방도 25
 
0.8%
산 23
 
0.7%
서원기(현 23
 
0.7%
Other values (1857) 2898
88.8%
2024-01-10T06:47:59.532429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1718
 
8.8%
0 789
 
4.1%
1 726
 
3.7%
( 551
 
2.8%
) 551
 
2.8%
선 537
 
2.8%
4 492
 
2.5%
2 474
 
2.4%
- 457
 
2.4%
5 404
 
2.1%
Other values (455) 12734
65.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10773
55.4%
Decimal Number 4323
22.2%
Space Separator 1718
 
8.8%
Open Punctuation 552
 
2.8%
Close Punctuation 551
 
2.8%
Lowercase Letter 544
 
2.8%
Dash Punctuation 457
 
2.4%
Other Punctuation 309
 
1.6%
Math Symbol 159
 
0.8%
Uppercase Letter 47
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
선 537
 
5.0%
도 369
 
3.4%
지 323
 
3.0%
동 266
 
2.5%
원 259
 
2.4%
산 253
 
2.3%
호 230
 
2.1%
상 226
 
2.1%
리 216
 
2.0%
아 201
 
1.9%
Other values (412) 7893
73.3%
Uppercase Letter
ValueCountFrequency (%)
C 10
21.3%
I 8
17.0%
S 5
10.6%
L 5
10.6%
K 4
 
8.5%
E 3
 
6.4%
H 3
 
6.4%
V 2
 
4.3%
D 2
 
4.3%
A 1
 
2.1%
Other values (4) 4
 
8.5%
Decimal Number
ValueCountFrequency (%)
0 789
18.3%
1 726
16.8%
4 492
11.4%
2 474
11.0%
5 404
9.3%
3 368
8.5%
8 329
7.6%
7 258
 
6.0%
6 253
 
5.9%
9 230
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 111
35.9%
# 64
20.7%
& 64
20.7%
; 64
20.7%
/ 5
 
1.6%
: 1
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
k 339
62.3%
m 201
36.9%
s 2
 
0.4%
d 1
 
0.2%
n 1
 
0.2%
Math Symbol
ValueCountFrequency (%)
~ 155
97.5%
= 3
 
1.9%
→ 1
 
0.6%
Open Punctuation
ValueCountFrequency (%)
( 551
99.8%
[ 1
 
0.2%
Space Separator
ValueCountFrequency (%)
1718
100.0%
Close Punctuation
ValueCountFrequency (%)
) 551
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 457
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10773
55.4%
Common 8069
41.5%
Latin 591
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
선 537
 
5.0%
도 369
 
3.4%
지 323
 
3.0%
동 266
 
2.5%
원 259
 
2.4%
산 253
 
2.3%
호 230
 
2.1%
상 226
 
2.1%
리 216
 
2.0%
아 201
 
1.9%
Other values (412) 7893
73.3%
Common
ValueCountFrequency (%)
1718
21.3%
0 789
9.8%
1 726
9.0%
( 551
 
6.8%
) 551
 
6.8%
4 492
 
6.1%
2 474
 
5.9%
- 457
 
5.7%
5 404
 
5.0%
3 368
 
4.6%
Other values (14) 1539
19.1%
Latin
ValueCountFrequency (%)
k 339
57.4%
m 201
34.0%
C 10
 
1.7%
I 8
 
1.4%
S 5
 
0.8%
L 5
 
0.8%
K 4
 
0.7%
E 3
 
0.5%
H 3
 
0.5%
V 2
 
0.3%
Other values (9) 11
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10773
55.4%
ASCII 8659
44.6%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1718
19.8%
0 789
 
9.1%
1 726
 
8.4%
( 551
 
6.4%
) 551
 
6.4%
4 492
 
5.7%
2 474
 
5.5%
- 457
 
5.3%
5 404
 
4.7%
3 368
 
4.2%
Other values (32) 2129
24.6%
Hangul
ValueCountFrequency (%)
선 537
 
5.0%
도 369
 
3.4%
지 323
 
3.0%
동 266
 
2.5%
원 259
 
2.4%
산 253
 
2.3%
호 230
 
2.1%
상 226
 
2.1%
리 216
 
2.0%
아 201
 
1.9%
Other values (412) 7893
73.3%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9424
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1489995 × 109
Minimum1.111 × 109
Maximum5.18 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:47:59.658843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111 × 109
5-th percentile1.168 × 109
Q14.1650001 × 109
median4.3770001 × 109
Q34.7170001 × 109
95-th percentile4.885 × 109
Maximum5.18 × 109
Range4.069 × 109
Interquartile range (IQR)5.5200004 × 108

Descriptive statistics

Standard deviation9.2554612 × 108
Coefficient of variation (CV)0.22307694
Kurtosis3.7503391
Mean4.1489995 × 109
Median Absolute Deviation (MAD)2.9600001 × 108
Skewness-2.0880176
Sum4.1489995 × 1013
Variance8.5663561 × 1017
MonotonicityNot monotonic
2024-01-10T06:47:59.773063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4873000097 3
 
< 0.1%
4885000295 3
 
< 0.1%
3171000015 3
 
< 0.1%
4146300127 3
 
< 0.1%
4315000203 3
 
< 0.1%
4272000223 3
 
< 0.1%
4372000027 3
 
< 0.1%
4223000496 3
 
< 0.1%
4775000017 3
 
< 0.1%
4872000348 3
 
< 0.1%
Other values (9414) 9970
99.7%
ValueCountFrequency (%)
1111000005 1
< 0.1%
1111000019 1
< 0.1%
1111000024 1
< 0.1%
1111000029 1
< 0.1%
1111000030 1
< 0.1%
1111000032 1
< 0.1%
1111000033 1
< 0.1%
1111000035 1
< 0.1%
1111000036 1
< 0.1%
1111000037 1
< 0.1%
ValueCountFrequency (%)
5180000023 1
< 0.1%
5180000021 1
< 0.1%
5180000019 1
< 0.1%
5180000018 1
< 0.1%
5180000015 1
< 0.1%
5180000013 1
< 0.1%
5180000010 1
< 0.1%
5180000008 1
< 0.1%
5180000006 1
< 0.1%
5180000005 1
< 0.1%

Interactions

2024-01-10T06:47:56.973292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:47:56.059750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:47:56.376557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:47:56.679955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:47:57.267986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:47:56.142622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:47:56.454523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:47:56.757116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:47:57.347002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:47:56.218534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:47:56.523908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:47:56.826595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:47:57.427190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:47:56.292719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:47:56.594402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:47:56.894486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:47:59.852943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역코드비탈면용도(보호목적)코드관리주체구분일련번호
지역코드1.0000.3180.1930.998
비탈면용도(보호목적)코드0.3181.0000.2730.309
관리주체구분0.1930.2731.0000.193
일련번호0.9980.3090.1931.000
2024-01-10T06:47:59.928629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역코드비탈면용도(보호목적)코드관리주체구분일련번호
지역코드1.000-0.013-0.1300.598
비탈면용도(보호목적)코드-0.0131.0000.215-0.121
관리주체구분-0.1300.2151.000-0.115
일련번호0.598-0.121-0.1151.000

Missing values

2024-01-10T06:47:57.544903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:47:57.630084image/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.
2024-01-10T06:47:57.709480image/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
1119226290107002용호4동목련맨션사면8<NA>2629000058
865311380105002서울 은평 구산 N1지구8<NA>1138000052
159941465105001경기 용인 수지 신봉 N3지구1<NA>4146500059
1914747820253291청도 청도 원정 N2지구1<NA>4782000097
1294343800330321만종지구1<NA>4380000146
120624713025624<NA>경부고속545<NA>4713000044
671848240370211금진1<NA>4824000024
152741461105001경기 용인 처인 유방 N28지구1<NA>4146100093
77284615033026<NA>전라선445<NA>4615000121
707947920340271경북 봉화 춘양 서벽 N4지구9<NA>4792000338
지역코드비탈면용도(보호목적)코드급경사지명관리주체구분주소일련번호
356644250310211계룡281<NA>4425000028
1298251810250291강원 인제 인제 가리산 N1지구1<NA>4281000244
4714311325033<NA>종합시험선55<NA>4311300060
5044313037031<NA>충북 중부145<NA>4313000223
989747290330211동부지구1자인시외버스터미널 뒷편4729000013
122944713025626<NA>경부고속665<NA>4713000056
1158444200350301음봉 쌍암1<NA>4420000016
1765651130360221강원 원주 귀래 귀래N1지구1<NA>4213000192
552546880250245충무1<NA>4688000124
138741210105001음배고개1<NA>4121000020

Duplicate rows

Most frequently occurring

지역코드비탈면용도(보호목적)코드급경사지명관리주체구분주소일련번호# duplicates
1926350101003반송동 2598<NA>26350001113
8331710400231출강리(362-10)1<NA>31710000153
10241430105001엘지아파트99<NA>41430000163
1224146310600<NA>경기 용인 기흥 지곡 N4지구8<NA>41463001273
12841465105006홍천중학교 급경사지1(홍천중학교)41465000093
13041465107002경기 용인 수지 상현 N2지구8<NA>41465000823
1654315012500<NA>천남5지구5<NA>43150002033
17143720400256대원1지구8<NA>43720000273
25046730370331둔사제 이설도로3<NA>46730000943
29747750320311대전41<NA>47750000173