Overview

Dataset statistics

Number of variables9
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory79.3 B

Variable types

Numeric5
Text2
Categorical2

Alerts

지하차도/교량/터널 has constant value ""Constant
id is highly overall correlated with 일반도로/고속도로 and 1 other fieldsHigh correlation
SD_CD is highly overall correlated with SGG_CD and 1 other fieldsHigh correlation
SGG_CD is highly overall correlated with SD_CD and 1 other fieldsHigh correlation
일반도로/고속도로 is highly overall correlated with id and 1 other fieldsHigh correlation
도로 is highly overall correlated with id and 1 other fieldsHigh correlation
SD_NM is highly overall correlated with SD_CD and 1 other fieldsHigh correlation
id has unique valuesUnique
gid has unique valuesUnique
일반도로/고속도로 has unique valuesUnique
도로 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:36:23.634865
Analysis finished2023-12-10 13:36:27.485342
Duration3.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:36:27.590385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T22:36:27.790713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

gid
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:36:28.230811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600
Distinct characters17
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

Unique100 ?
Unique (%)100.0%

Sample

1st row다마8286
2nd row나바9386
3rd row다사6165
4th row다다1395
5th row라바8200
ValueCountFrequency (%)
다마8286 1
 
1.0%
다마9603 1
 
1.0%
다라4205 1
 
1.0%
라라5162 1
 
1.0%
라마2468 1
 
1.0%
다라5528 1
 
1.0%
마마1753 1
 
1.0%
라마0910 1
 
1.0%
라사8924 1
 
1.0%
마마2623 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T22:36:28.861340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
라 56
 
9.3%
마 51
 
8.5%
9 49
 
8.2%
6 47
 
7.8%
3 45
 
7.5%
다 41
 
6.8%
2 41
 
6.8%
1 39
 
6.5%
0 38
 
6.3%
5 38
 
6.3%
Other values (7) 155
25.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
66.7%
Other Letter 200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 49
12.2%
6 47
11.8%
3 45
11.2%
2 41
10.2%
1 39
9.8%
0 38
9.5%
5 38
9.5%
8 37
9.2%
7 36
9.0%
4 30
7.5%
Other Letter
ValueCountFrequency (%)
라 56
28.0%
마 51
25.5%
다 41
20.5%
바 24
12.0%
사 19
 
9.5%
나 6
 
3.0%
아 3
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 400
66.7%
Hangul 200
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
9 49
12.2%
6 47
11.8%
3 45
11.2%
2 41
10.2%
1 39
9.8%
0 38
9.5%
5 38
9.5%
8 37
9.2%
7 36
9.0%
4 30
7.5%
Hangul
ValueCountFrequency (%)
라 56
28.0%
마 51
25.5%
다 41
20.5%
바 24
12.0%
사 19
 
9.5%
나 6
 
3.0%
아 3
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
66.7%
Hangul 200
33.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
라 56
28.0%
마 51
25.5%
다 41
20.5%
바 24
12.0%
사 19
 
9.5%
나 6
 
3.0%
아 3
 
1.5%
ASCII
ValueCountFrequency (%)
9 49
12.2%
6 47
11.8%
3 45
11.2%
2 41
10.2%
1 39
9.8%
0 38
9.5%
5 38
9.5%
8 37
9.2%
7 36
9.0%
4 30
7.5%

SD_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.68
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:36:29.065842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile29.9
Q142
median45
Q347
95-th percentile48
Maximum50
Range39
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.6262013
Coefficient of variation (CV)0.12880498
Kurtosis13.045429
Mean43.68
Median Absolute Deviation (MAD)2
Skewness-3.2073491
Sum4368
Variance31.654141
MonotonicityNot monotonic
2023-12-10T22:36:29.261452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
47 20
20.0%
46 17
17.0%
42 16
16.0%
45 9
9.0%
44 9
9.0%
48 8
 
8.0%
43 8
 
8.0%
41 4
 
4.0%
50 2
 
2.0%
28 2
 
2.0%
Other values (5) 5
 
5.0%
ValueCountFrequency (%)
11 1
 
1.0%
26 1
 
1.0%
27 1
 
1.0%
28 2
 
2.0%
30 1
 
1.0%
31 1
 
1.0%
41 4
 
4.0%
42 16
16.0%
43 8
8.0%
44 9
9.0%
ValueCountFrequency (%)
50 2
 
2.0%
48 8
 
8.0%
47 20
20.0%
46 17
17.0%
45 9
9.0%
44 9
9.0%
43 8
 
8.0%
42 16
16.0%
41 4
 
4.0%
31 1
 
1.0%

SD_NM
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경북
20 
전남
17 
강원
16 
전북
9 
충남
9 
Other values (10)
29 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique5 ?
Unique (%)5.0%

Sample

1st row전북
2nd row충남
3rd row서울
4th row전남
5th row경북

Common Values

ValueCountFrequency (%)
경북 20
20.0%
전남 17
17.0%
강원 16
16.0%
전북 9
9.0%
충남 9
9.0%
경남 8
 
8.0%
충북 8
 
8.0%
경기 4
 
4.0%
제주 2
 
2.0%
인천 2
 
2.0%
Other values (5) 5
 
5.0%

Length

2023-12-10T22:36:29.435048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경북 20
20.0%
전남 17
17.0%
강원 16
16.0%
전북 9
9.0%
충남 9
9.0%
경남 8
 
8.0%
충북 8
 
8.0%
경기 4
 
4.0%
제주 2
 
2.0%
인천 2
 
2.0%
Other values (5) 5
 
5.0%

SGG_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44212.77
Minimum11350
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:36:29.598095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11350
5-th percentile30068.5
Q142805
median45720
Q347175
95-th percentile48850
Maximum50130
Range38780
Interquartile range (IQR)4370

Descriptive statistics

Standard deviation5671.3089
Coefficient of variation (CV)0.1282731
Kurtosis12.971987
Mean44212.77
Median Absolute Deviation (MAD)1975
Skewness-3.2122878
Sum4421277
Variance32163744
MonotonicityNot monotonic
2023-12-10T22:36:29.820992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46820 5
 
5.0%
47830 3
 
3.0%
47230 3
 
3.0%
42720 3
 
3.0%
47130 3
 
3.0%
45710 2
 
2.0%
47170 2
 
2.0%
44810 2
 
2.0%
46170 2
 
2.0%
43130 2
 
2.0%
Other values (64) 73
73.0%
ValueCountFrequency (%)
11350 1
1.0%
26440 1
1.0%
27230 1
1.0%
28260 1
1.0%
28710 1
1.0%
30140 1
1.0%
31200 1
1.0%
41450 1
1.0%
41500 1
1.0%
41550 1
1.0%
ValueCountFrequency (%)
50130 2
2.0%
48890 1
 
1.0%
48860 1
 
1.0%
48850 2
2.0%
48740 1
 
1.0%
48270 1
 
1.0%
48240 1
 
1.0%
48170 1
 
1.0%
47920 1
 
1.0%
47830 3
3.0%

SGG_KOR_NM
Text

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:36:30.166848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.05
Min length2

Characters and Unicode

Total characters305
Distinct characters79
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

Unique53 ?
Unique (%)53.0%

Sample

1st row완주군
2nd row태안군
3rd row노원구
4th row해남군
5th row구미시
ValueCountFrequency (%)
해남군 5
 
5.0%
홍천군 3
 
3.0%
경주시 3
 
3.0%
고령군 3
 
3.0%
영천시 3
 
3.0%
청송군 2
 
2.0%
경산시 2
 
2.0%
완주군 2
 
2.0%
북구 2
 
2.0%
나주시 2
 
2.0%
Other values (64) 74
73.3%
2023-12-10T22:36:30.690222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 57
18.7%
시 38
 
12.5%
천 15
 
4.9%
주 13
 
4.3%
구 10
 
3.3%
남 8
 
2.6%
안 8
 
2.6%
산 8
 
2.6%
영 6
 
2.0%
경 6
 
2.0%
Other values (69) 136
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 304
99.7%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 57
18.8%
시 38
 
12.5%
천 15
 
4.9%
주 13
 
4.3%
구 10
 
3.3%
남 8
 
2.6%
안 8
 
2.6%
산 8
 
2.6%
영 6
 
2.0%
경 6
 
2.0%
Other values (68) 135
44.4%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 304
99.7%
Common 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 57
18.8%
시 38
 
12.5%
천 15
 
4.9%
주 13
 
4.3%
구 10
 
3.3%
남 8
 
2.6%
안 8
 
2.6%
산 8
 
2.6%
영 6
 
2.0%
경 6
 
2.0%
Other values (68) 135
44.4%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 304
99.7%
ASCII 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 57
18.8%
시 38
 
12.5%
천 15
 
4.9%
주 13
 
4.3%
구 10
 
3.3%
남 8
 
2.6%
안 8
 
2.6%
산 8
 
2.6%
영 6
 
2.0%
경 6
 
2.0%
Other values (68) 135
44.4%
ASCII
ValueCountFrequency (%)
1
100.0%

일반도로/고속도로
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0057223267
Minimum0.00023931
Maximum0.01019906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:36:30.904796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00023931
5-th percentile0.0008027928
Q10.003653822
median0.0057522115
Q30.0083545082
95-th percentile0.0099327329
Maximum0.01019906
Range0.00995975
Interquartile range (IQR)0.0047006862

Descriptive statistics

Standard deviation0.0028526285
Coefficient of variation (CV)0.4985085
Kurtosis-1.0448972
Mean0.0057223267
Median Absolute Deviation (MAD)0.002175679
Skewness-0.11822251
Sum0.57223267
Variance8.1374893 × 10-6
MonotonicityStrictly increasing
2023-12-10T22:36:31.095194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00023931 1
 
1.0%
0.007103889 1
 
1.0%
0.008326896 1
 
1.0%
0.007890344 1
 
1.0%
0.007815098 1
 
1.0%
0.007806502 1
 
1.0%
0.007766837 1
 
1.0%
0.007489912 1
 
1.0%
0.007461437 1
 
1.0%
0.007341842 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.00023931 1
1.0%
0.000391037 1
1.0%
0.00046512 1
1.0%
0.000608548 1
1.0%
0.000760305 1
1.0%
0.000805029 1
1.0%
0.000960845 1
1.0%
0.001131257 1
1.0%
0.001792492 1
1.0%
0.001826635 1
1.0%
ValueCountFrequency (%)
0.01019906 1
1.0%
0.010191202 1
1.0%
0.010079406 1
1.0%
0.010014088 1
1.0%
0.009982283 1
1.0%
0.009930125 1
1.0%
0.009905867 1
1.0%
0.009884294 1
1.0%
0.009800665 1
1.0%
0.009652835 1
1.0%

지하차도/교량/터널
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-99
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-99
2nd row-99
3rd row-99
4th row-99
5th row-99

Common Values

ValueCountFrequency (%)
-99 100
100.0%

Length

2023-12-10T22:36:31.277298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:31.741454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
99 100
100.0%

도로
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0057223267
Minimum0.00023931
Maximum0.01019906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:36:31.871665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00023931
5-th percentile0.0008027928
Q10.003653822
median0.0057522115
Q30.0083545082
95-th percentile0.0099327329
Maximum0.01019906
Range0.00995975
Interquartile range (IQR)0.0047006862

Descriptive statistics

Standard deviation0.0028526285
Coefficient of variation (CV)0.4985085
Kurtosis-1.0448972
Mean0.0057223267
Median Absolute Deviation (MAD)0.002175679
Skewness-0.11822251
Sum0.57223267
Variance8.1374893 × 10-6
MonotonicityStrictly increasing
2023-12-10T22:36:32.059468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00023931 1
 
1.0%
0.007103889 1
 
1.0%
0.008326896 1
 
1.0%
0.007890344 1
 
1.0%
0.007815098 1
 
1.0%
0.007806502 1
 
1.0%
0.007766837 1
 
1.0%
0.007489912 1
 
1.0%
0.007461437 1
 
1.0%
0.007341842 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.00023931 1
1.0%
0.000391037 1
1.0%
0.00046512 1
1.0%
0.000608548 1
1.0%
0.000760305 1
1.0%
0.000805029 1
1.0%
0.000960845 1
1.0%
0.001131257 1
1.0%
0.001792492 1
1.0%
0.001826635 1
1.0%
ValueCountFrequency (%)
0.01019906 1
1.0%
0.010191202 1
1.0%
0.010079406 1
1.0%
0.010014088 1
1.0%
0.009982283 1
1.0%
0.009930125 1
1.0%
0.009905867 1
1.0%
0.009884294 1
1.0%
0.009800665 1
1.0%
0.009652835 1
1.0%

Interactions

2023-12-10T22:36:26.575995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:24.033461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:24.600414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:25.189234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:25.910397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:26.685129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:24.153891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:24.715871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:25.290710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:26.039200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:26.818225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:24.267007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:24.846421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:25.495708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:26.181131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:26.943862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:24.385162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:24.960796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:25.681769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:26.327586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:27.060750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:24.493152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:25.072476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:25.787938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:26.462475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:36:32.199215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idgidSD_CDSD_NMSGG_CDSGG_KOR_NM일반도로/고속도로도로
id1.0001.0000.1270.0000.2080.6020.9730.973
gid1.0001.0001.0001.0001.0001.0001.0001.000
SD_CD0.1271.0001.0001.0000.9930.9630.2350.235
SD_NM0.0001.0001.0001.0000.9780.9900.2960.296
SGG_CD0.2081.0000.9930.9781.0000.9550.0000.000
SGG_KOR_NM0.6021.0000.9630.9900.9551.0000.6500.650
일반도로/고속도로0.9731.0000.2350.2960.0000.6501.0001.000
도로0.9731.0000.2350.2960.0000.6501.0001.000
2023-12-10T22:36:32.352426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idSD_CDSGG_CD일반도로/고속도로도로SD_NM
id1.0000.0150.0351.0001.0000.000
SD_CD0.0151.0000.9900.0150.0150.956
SGG_CD0.0350.9901.0000.0350.0350.900
일반도로/고속도로1.0000.0150.0351.0001.0000.104
도로1.0000.0150.0351.0001.0000.104
SD_NM0.0000.9560.9000.1040.1041.000

Missing values

2023-12-10T22:36:27.231756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:36:27.415154image/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

idgidSD_CDSD_NMSGG_CDSGG_KOR_NM일반도로/고속도로지하차도/교량/터널도로
01다마828645전북45710완주군0.000239-990.000239
12나바938644충남44825태안군0.000391-990.000391
23다사616511서울11350노원구0.000465-990.000465
34다다139546전남46820해남군0.000609-990.000609
45라바820047경북47190구미시0.00076-990.00076
56다마925645전북45720진안군0.000805-990.000805
67마마236847경북47230영천시0.000961-990.000961
78라사698442강원42720홍천군0.001131-990.001131
89라사963342강원42770정선군0.001792-990.001792
910마마279947경북47230영천시0.001827-990.001827
idgidSD_CDSD_NMSGG_CDSGG_KOR_NM일반도로/고속도로지하차도/교량/터널도로
9091라마714047경북47830고령군0.009653-990.009653
9192다다409946전남46890완도군0.009801-990.009801
9293라라128848경남48850하동군0.009884-990.009884
9394다라224146전남46830영암군0.009906-990.009906
9495다마968344충남44710금산군0.00993-990.00993
9596라사243142강원42130원주시0.009982-990.009982
9697마바106847경북47920봉화군0.010014-990.010014
9798다마535145전북45210김제시0.010079-990.010079
9899마마197347경북47290경산시0.010191-990.010191
99100다사898441경기41820가평군0.010199-990.010199