Overview

Dataset statistics

Number of variables8
Number of observations151
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.3 KiB
Average record size in memory69.9 B

Variable types

Numeric5
Text2
Categorical1

Dataset

Description국립공원공단에서 관리하고 있는 151개 기상관측시설(AWS, 강수량계)에 대한 표준지점번호, 기관지점번호, 지점명, 주소, 위도, 경도, 고도에 대한 정보입니다.
Author국립공원공단
URLhttps://www.data.go.kr/data/15062932/fileData.do

Alerts

표준지점번호 is highly overall correlated with 위도High correlation
기관지점번호 is highly overall correlated with 장비구분High correlation
위도 is highly overall correlated with 표준지점번호 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도High correlation
장비구분 is highly overall correlated with 기관지점번호High correlation
표준지점번호 has unique valuesUnique
기관지점번호 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:31:24.390041
Analysis finished2023-12-12 06:31:27.793718
Duration3.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

표준지점번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct151
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5759.053
Minimum1920
Maximum8938
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T15:31:27.884909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1920
5-th percentile2922.5
Q13924.5
median5936
Q36953.5
95-th percentile8929.5
Maximum8938
Range7018
Interquartile range (IQR)3029

Descriptive statistics

Standard deviation2143.9299
Coefficient of variation (CV)0.37227127
Kurtosis-1.2927581
Mean5759.053
Median Absolute Deviation (MAD)1996
Skewness-0.16508987
Sum869617
Variance4596435.6
MonotonicityStrictly increasing
2023-12-12T15:31:28.088368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1920 1
 
0.7%
6945 1
 
0.7%
6938 1
 
0.7%
6939 1
 
0.7%
6940 1
 
0.7%
6941 1
 
0.7%
6942 1
 
0.7%
6943 1
 
0.7%
6944 1
 
0.7%
6946 1
 
0.7%
Other values (141) 141
93.4%
ValueCountFrequency (%)
1920 1
0.7%
1921 1
0.7%
1922 1
0.7%
1923 1
0.7%
1924 1
0.7%
2920 1
0.7%
2921 1
0.7%
2922 1
0.7%
2923 1
0.7%
2924 1
0.7%
ValueCountFrequency (%)
8938 1
0.7%
8937 1
0.7%
8936 1
0.7%
8935 1
0.7%
8934 1
0.7%
8932 1
0.7%
8931 1
0.7%
8930 1
0.7%
8929 1
0.7%
8928 1
0.7%

기관지점번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct151
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140329.95
Minimum11006
Maximum331002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T15:31:28.275450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11006
5-th percentile37001.5
Q151004.5
median103001
Q3235101
95-th percentile315001.5
Maximum331002
Range319996
Interquartile range (IQR)184096.5

Descriptive statistics

Standard deviation93807.087
Coefficient of variation (CV)0.66847518
Kurtosis-1.337309
Mean140329.95
Median Absolute Deviation (MAD)61997
Skewness0.42108269
Sum21189822
Variance8.7997696 × 109
MonotonicityNot monotonic
2023-12-12T15:31:28.469665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
235200 1
 
0.7%
249002 1
 
0.7%
215002 1
 
0.7%
316001 1
 
0.7%
249001 1
 
0.7%
216001 1
 
0.7%
110001 1
 
0.7%
210001 1
 
0.7%
220001 1
 
0.7%
216002 1
 
0.7%
Other values (141) 141
93.4%
ValueCountFrequency (%)
11006 1
0.7%
21005 1
0.7%
32001 1
0.7%
35001 1
0.7%
35002 1
0.7%
36001 1
0.7%
36002 1
0.7%
37001 1
0.7%
37002 1
0.7%
38001 1
0.7%
ValueCountFrequency (%)
331002 1
0.7%
331001 1
0.7%
322002 1
0.7%
322001 1
0.7%
316003 1
0.7%
316002 1
0.7%
316001 1
0.7%
315002 1
0.7%
315001 1
0.7%
255008 1
0.7%

지점명
Text

Distinct150
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T15:31:28.828390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.1986755
Min length2

Characters and Unicode

Total characters483
Distinct characters160
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)98.7%

Sample

1st row수유
2nd row북한산성
3rd row구캡소대
4th row오봉삼거리
5th row송추
ValueCountFrequency (%)
상선암 2
 
1.3%
문수사2 1
 
0.7%
새재 1
 
0.7%
소모도 1
 
0.7%
비금도초 1
 
0.7%
남창 1
 
0.7%
경포대 1
 
0.7%
원효 1
 
0.7%
담양 1
 
0.7%
고흥 1
 
0.7%
Other values (140) 140
92.7%
2023-12-12T15:31:29.400027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
봉 23
 
4.8%
사 16
 
3.3%
산 15
 
3.1%
골 13
 
2.7%
대 12
 
2.5%
수 12
 
2.5%
원 11
 
2.3%
소 11
 
2.3%
동 10
 
2.1%
령 9
 
1.9%
Other values (150) 351
72.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 464
96.1%
Decimal Number 19
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
봉 23
 
5.0%
사 16
 
3.4%
산 15
 
3.2%
골 13
 
2.8%
대 12
 
2.6%
수 12
 
2.6%
원 11
 
2.4%
소 11
 
2.4%
동 10
 
2.2%
령 9
 
1.9%
Other values (146) 332
71.6%
Decimal Number
ValueCountFrequency (%)
1 8
42.1%
2 8
42.1%
3 2
 
10.5%
4 1
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 464
96.1%
Common 19
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
봉 23
 
5.0%
사 16
 
3.4%
산 15
 
3.2%
골 13
 
2.8%
대 12
 
2.6%
수 12
 
2.6%
원 11
 
2.4%
소 11
 
2.4%
동 10
 
2.2%
령 9
 
1.9%
Other values (146) 332
71.6%
Common
ValueCountFrequency (%)
1 8
42.1%
2 8
42.1%
3 2
 
10.5%
4 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 464
96.1%
ASCII 19
 
3.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
봉 23
 
5.0%
사 16
 
3.4%
산 15
 
3.2%
골 13
 
2.8%
대 12
 
2.6%
수 12
 
2.6%
원 11
 
2.4%
소 11
 
2.4%
동 10
 
2.2%
령 9
 
1.9%
Other values (146) 332
71.6%
ASCII
ValueCountFrequency (%)
1 8
42.1%
2 8
42.1%
3 2
 
10.5%
4 1
 
5.3%

주소
Text

Distinct138
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T15:31:29.823982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length35
Mean length22.927152
Min length15

Characters and Unicode

Total characters3462
Distinct characters206
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)83.4%

Sample

1st row서울시 강북구 수유4동 산 73-1 수유분소
2nd row서울시 은평구 진관동 264-2 북한산성분소
3rd row경기도 양주시 장흥면 울대리 산66-19
4th row경기도 양주시 장흥면 울대리 산66-1
5th row경기도 양주시 장흥면 울대리 477
ValueCountFrequency (%)
전라남도 34
 
4.3%
강원도 21
 
2.6%
충청북도 21
 
2.6%
구례군 20
 
2.5%
산 19
 
2.4%
경상북도 14
 
1.8%
전라북도 11
 
1.4%
단양군 10
 
1.3%
산동면 9
 
1.1%
경상남도 8
 
1.0%
Other values (385) 628
79.0%
2023-12-12T15:31:30.400698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
654
 
18.9%
산 142
 
4.1%
1 138
 
4.0%
리 135
 
3.9%
면 129
 
3.7%
도 125
 
3.6%
군 102
 
2.9%
- 86
 
2.5%
남 78
 
2.3%
2 77
 
2.2%
Other values (196) 1796
51.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2221
64.2%
Space Separator 654
 
18.9%
Decimal Number 458
 
13.2%
Dash Punctuation 86
 
2.5%
Close Punctuation 21
 
0.6%
Open Punctuation 21
 
0.6%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 142
 
6.4%
리 135
 
6.1%
면 129
 
5.8%
도 125
 
5.6%
군 102
 
4.6%
남 78
 
3.5%
북 75
 
3.4%
전 66
 
3.0%
동 51
 
2.3%
시 51
 
2.3%
Other values (181) 1267
57.0%
Decimal Number
ValueCountFrequency (%)
1 138
30.1%
2 77
16.8%
6 43
 
9.4%
3 38
 
8.3%
0 33
 
7.2%
4 28
 
6.1%
7 28
 
6.1%
8 27
 
5.9%
5 25
 
5.5%
9 21
 
4.6%
Space Separator
ValueCountFrequency (%)
654
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2221
64.2%
Common 1241
35.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 142
 
6.4%
리 135
 
6.1%
면 129
 
5.8%
도 125
 
5.6%
군 102
 
4.6%
남 78
 
3.5%
북 75
 
3.4%
전 66
 
3.0%
동 51
 
2.3%
시 51
 
2.3%
Other values (181) 1267
57.0%
Common
ValueCountFrequency (%)
654
52.7%
1 138
 
11.1%
- 86
 
6.9%
2 77
 
6.2%
6 43
 
3.5%
3 38
 
3.1%
0 33
 
2.7%
4 28
 
2.3%
7 28
 
2.3%
8 27
 
2.2%
Other values (5) 89
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2221
64.2%
ASCII 1241
35.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
654
52.7%
1 138
 
11.1%
- 86
 
6.9%
2 77
 
6.2%
6 43
 
3.5%
3 38
 
3.1%
0 33
 
2.7%
4 28
 
2.3%
7 28
 
2.3%
8 27
 
2.2%
Other values (5) 89
 
7.2%
Hangul
ValueCountFrequency (%)
산 142
 
6.4%
리 135
 
6.1%
면 129
 
5.8%
도 125
 
5.6%
군 102
 
4.6%
남 78
 
3.5%
북 75
 
3.4%
전 66
 
3.0%
동 51
 
2.3%
시 51
 
2.3%
Other values (181) 1267
57.0%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct151
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.242075
Minimum34.22699
Maximum38.19185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T15:31:30.604328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.22699
5-th percentile34.756595
Q135.316505
median36.34472
Q336.986945
95-th percentile38.10455
Maximum38.19185
Range3.96486
Interquartile range (IQR)1.67044

Descriptive statistics

Standard deviation1.0609355
Coefficient of variation (CV)0.029273586
Kurtosis-1.12452
Mean36.242075
Median Absolute Deviation (MAD)0.98034
Skewness0.23940008
Sum5472.5533
Variance1.1255841
MonotonicityNot monotonic
2023-12-12T15:31:31.081423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.6432 1
 
0.7%
34.63648 1
 
0.7%
35.47721 1
 
0.7%
34.22699 1
 
0.7%
34.6914 1
 
0.7%
35.45925 1
 
0.7%
34.7458 1
 
0.7%
35.14417 1
 
0.7%
35.13812 1
 
0.7%
35.41885 1
 
0.7%
Other values (141) 141
93.4%
ValueCountFrequency (%)
34.22699 1
0.7%
34.2378 1
0.7%
34.51583 1
0.7%
34.59998 1
0.7%
34.63648 1
0.7%
34.6914 1
0.7%
34.7458 1
0.7%
34.75375 1
0.7%
34.75944 1
0.7%
34.76139 1
0.7%
ValueCountFrequency (%)
38.19185 1
0.7%
38.17748 1
0.7%
38.15981 1
0.7%
38.15555 1
0.7%
38.14666 1
0.7%
38.12659 1
0.7%
38.11941 1
0.7%
38.10964 1
0.7%
38.09946 1
0.7%
38.08972 1
0.7%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct151
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.7903
Minimum125.9296
Maximum129.20306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T15:31:31.301486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.9296
5-th percentile126.64059
Q1127.495
median127.7589
Q3128.36355
95-th percentile128.936
Maximum129.20306
Range3.27346
Interquartile range (IQR)0.86855

Descriptive statistics

Standard deviation0.71006759
Coefficient of variation (CV)0.0055565063
Kurtosis-0.32766403
Mean127.7903
Median Absolute Deviation (MAD)0.53289
Skewness-0.20643585
Sum19296.335
Variance0.50419598
MonotonicityNot monotonic
2023-12-12T15:31:31.510093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0025 1
 
0.7%
127.41696 1
 
0.7%
126.84217 1
 
0.7%
126.77139 1
 
0.7%
125.9296 1
 
0.7%
126.84087 1
 
0.7%
126.7094 1
 
0.7%
126.98944 1
 
0.7%
127.02014 1
 
0.7%
126.87896 1
 
0.7%
Other values (141) 141
93.4%
ValueCountFrequency (%)
125.9296 1
0.7%
126.0594 1
0.7%
126.20365 1
0.7%
126.32163 1
0.7%
126.37701 1
0.7%
126.48734 1
0.7%
126.57393 1
0.7%
126.58757 1
0.7%
126.69361 1
0.7%
126.69806 1
0.7%
ValueCountFrequency (%)
129.20306 1
0.7%
129.19981 1
0.7%
129.19481 1
0.7%
129.19122 1
0.7%
129.18341 1
0.7%
129.16115 1
0.7%
129.00419 1
0.7%
128.93935 1
0.7%
128.93265 1
0.7%
128.91682 1
0.7%

고도(m)
Real number (ℝ)

Distinct132
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean659.37748
Minimum6
Maximum1640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T15:31:31.709114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile30.5
Q1315
median605
Q3996
95-th percentile1427.5
Maximum1640
Range1634
Interquartile range (IQR)681

Descriptive statistics

Standard deviation442.71039
Coefficient of variation (CV)0.67140659
Kurtosis-0.88034489
Mean659.37748
Median Absolute Deviation (MAD)318
Skewness0.37669935
Sum99566
Variance195992.49
MonotonicityNot monotonic
2023-12-12T15:31:31.905506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
620 3
 
2.0%
900 2
 
1.3%
32 2
 
1.3%
370 2
 
1.3%
510 2
 
1.3%
416 2
 
1.3%
115 2
 
1.3%
923 2
 
1.3%
217 2
 
1.3%
650 2
 
1.3%
Other values (122) 130
86.1%
ValueCountFrequency (%)
6 2
1.3%
7 2
1.3%
10 1
0.7%
16 1
0.7%
22 1
0.7%
29 1
0.7%
32 2
1.3%
35 1
0.7%
51 1
0.7%
76 1
0.7%
ValueCountFrequency (%)
1640 1
0.7%
1586 1
0.7%
1560 1
0.7%
1550 1
0.7%
1520 1
0.7%
1492 1
0.7%
1447 1
0.7%
1430 1
0.7%
1425 1
0.7%
1418 1
0.7%

장비구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
강수량계
83 
AWS
68 

Length

Max length4
Median length4
Mean length3.5496689
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAWS
2nd rowAWS
3rd row강수량계
4th row강수량계
5th rowAWS

Common Values

ValueCountFrequency (%)
강수량계 83
55.0%
AWS 68
45.0%

Length

2023-12-12T15:31:32.075224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:31:32.218855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강수량계 83
55.0%
aws 68
45.0%

Interactions

2023-12-12T15:31:26.897735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:24.830483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:25.341519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:25.826262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:26.334121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:27.027412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:24.941931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:25.432496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:25.919042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:26.461107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:27.145989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:25.054188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:25.534483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:26.016720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:26.557130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:27.281019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:25.153756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:25.647437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:26.122881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:26.687579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:27.409338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:25.245124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:25.733163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:26.224249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:31:26.792046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:31:32.317458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준지점번호기관지점번호위도경도고도(m)장비구분
표준지점번호1.0000.6220.8450.7540.4520.149
기관지점번호0.6221.0000.8110.6990.4340.608
위도0.8450.8111.0000.8870.5600.310
경도0.7540.6990.8871.0000.5070.252
고도(m)0.4520.4340.5600.5071.0000.618
장비구분0.1490.6080.3100.2520.6181.000
2023-12-12T15:31:32.450707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준지점번호기관지점번호위도경도고도(m)장비구분
표준지점번호1.0000.099-0.677-0.1570.1470.120
기관지점번호0.0991.000-0.067-0.146-0.2870.601
위도-0.677-0.0671.0000.6250.1130.231
경도-0.157-0.1460.6251.0000.3630.187
고도(m)0.147-0.2870.1130.3631.0000.465
장비구분0.1200.6010.2310.1870.4651.000

Missing values

2023-12-12T15:31:27.587735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:31:27.735720image/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

표준지점번호기관지점번호지점명주소위도경도고도(m)장비구분
01920235200수유서울시 강북구 수유4동 산 73-1 수유분소37.6432127.002596AWS
11921235201북한산성서울시 은평구 진관동 264-2 북한산성분소37.65439126.9508676AWS
21922235001구캡소대경기도 양주시 장흥면 울대리 산66-1937.71338127.01047378강수량계
31923235002오봉삼거리경기도 양주시 장흥면 울대리 산66-137.69761127.00653605강수량계
41924236001송추경기도 양주시 장흥면 울대리 47737.7126126.9809150AWS
5292046002성국사강원도 양양군 서면 오색리 산1-2538.07614128.43984378강수량계
6292146001한계령강원도 양양군 서면 오색리 산1-1238.08972128.42858553강수량계
7292247002저항령강원도 속초시 설악동 산3938.17748128.45498465강수량계
8292347001비선대강원도 속초시 설악동 산4138.15555128.47106423강수량계
92924101001약수강원도 원주시 소초면 학곡리 산 3337.36516128.048321090강수량계
표준지점번호기관지점번호지점명주소위도경도고도(m)장비구분
1418928255007하동경남 하동군 화개면 대성리 산 200-635.27813127.65265247AWS
1428929185003마장마을경남 합천군 가야면 치인리 산21-1임35.78925128.04667854강수량계
1438930185001가야산중봉경남 합천군 가야면 치인리 산1-1임35.81964128.115971175강수량계
1448931185002오봉산중턱경남 합천군 가야면 치인리 산16-1임35.77697128.09258650강수량계
1458932331002상왕봉경남 합천군 가야면 치인리 산1-1임35.81944128.116111142AWS
1468934255004세석경남 산청군 시천면 세석길 217-573(세석대피소)35.31828127.693481550AWS
147893551006명선봉전라북도 남원시 산내면 부운리 산12035.3287127.613351586AWS
148893642032산동1전라남도 구례군 산동면 위안리 산 21635.33272127.51581721강수량계
149893742033산동2전라남도 구례군 산동면 위안리 산 21635.33242127.50933751강수량계
1508938255008피아골대피소전라남도 구례군 토지면 내동리 산 36835.28603127.55611793AWS