Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory986.3 KiB
Average record size in memory101.0 B

Variable types

Numeric5
Categorical4
Text2

Dataset

Description전북특별자치도 진안군 도로명주소에 대한 정보입니다. 시도명, 시군구명, 법정읍면동명, 법정리명, 도로명 등 정보를 제공합니다.
Author전북특별자치도 진안군
URLhttps://www.data.go.kr/data/15112705/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
연번 is highly overall correlated with 법정읍면동명High correlation
법정읍면동명 is highly overall correlated with 연번High correlation
산여부 is highly imbalanced (81.5%)Imbalance
연번 has unique valuesUnique
지번부번 has 2796 (28.0%) zerosZeros
건물부번 has 4335 (43.4%) zerosZeros

Reproduction

Analysis started2024-03-15 00:19:56.197318
Analysis finished2024-03-15 00:20:05.478450
Duration9.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8439.5842
Minimum2
Maximum16889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T09:20:05.665492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile847.85
Q14229.5
median8441.5
Q312684.25
95-th percentile16022.05
Maximum16889
Range16887
Interquartile range (IQR)8454.75

Descriptive statistics

Standard deviation4877.9384
Coefficient of variation (CV)0.57798326
Kurtosis-1.1971578
Mean8439.5842
Median Absolute Deviation (MAD)4232
Skewness-0.0014102425
Sum84395842
Variance23794283
MonotonicityNot monotonic
2024-03-15T09:20:06.009961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16307 1
 
< 0.1%
8310 1
 
< 0.1%
12503 1
 
< 0.1%
10077 1
 
< 0.1%
6933 1
 
< 0.1%
6815 1
 
< 0.1%
7856 1
 
< 0.1%
9151 1
 
< 0.1%
9598 1
 
< 0.1%
8390 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
7 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
17 1
< 0.1%
19 1
< 0.1%
ValueCountFrequency (%)
16889 1
< 0.1%
16888 1
< 0.1%
16887 1
< 0.1%
16884 1
< 0.1%
16883 1
< 0.1%
16882 1
< 0.1%
16880 1
< 0.1%
16878 1
< 0.1%
16876 1
< 0.1%
16873 1
< 0.1%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
전북특별자치도
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전북특별자치도 10000
100.0%

Length

2024-03-15T09:20:06.342189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T09:20:06.576725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북특별자치도 10000
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
진안군
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
진안군 10000
100.0%

Length

2024-03-15T09:20:06.745178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T09:20:06.912017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
진안군 10000
100.0%

법정읍면동명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
진안읍
2707 
부귀면
1155 
백운면
973 
마령면
885 
성수면
884 
Other values (6)
3396 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row백운면
2nd row마령면
3rd row마령면
4th row백운면
5th row동향면

Common Values

ValueCountFrequency (%)
진안읍 2707
27.1%
부귀면 1155
11.6%
백운면 973
 
9.7%
마령면 885
 
8.8%
성수면 884
 
8.8%
주천면 832
 
8.3%
동향면 768
 
7.7%
안천면 537
 
5.4%
정천면 444
 
4.4%
용담면 421
 
4.2%

Length

2024-03-15T09:20:07.220331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
진안읍 2707
27.1%
부귀면 1155
11.6%
백운면 973
 
9.7%
마령면 885
 
8.8%
성수면 884
 
8.8%
주천면 832
 
8.3%
동향면 768
 
7.7%
안천면 537
 
5.4%
정천면 444
 
4.4%
용담면 421
 
4.2%

법정리명
Text

Distinct76
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T09:20:08.243753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남계리
2nd row계서리
3rd row덕천리
4th row운교리
5th row신송리
ValueCountFrequency (%)
군상리 620
 
6.2%
송풍리 321
 
3.2%
군하리 314
 
3.1%
연장리 302
 
3.0%
평지리 268
 
2.7%
덕천리 255
 
2.5%
구룡리 223
 
2.2%
능금리 217
 
2.2%
대불리 214
 
2.1%
물곡리 205
 
2.1%
Other values (66) 7061
70.6%
2024-03-15T09:20:09.700598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 10000
33.3%
군 934
 
3.1%
평 682
 
2.3%
신 644
 
2.1%
상 620
 
2.1%
산 618
 
2.1%
송 499
 
1.7%
양 481
 
1.6%
운 480
 
1.6%
덕 453
 
1.5%
Other values (71) 14589
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30000
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 10000
33.3%
군 934
 
3.1%
평 682
 
2.3%
신 644
 
2.1%
상 620
 
2.1%
산 618
 
2.1%
송 499
 
1.7%
양 481
 
1.6%
운 480
 
1.6%
덕 453
 
1.5%
Other values (71) 14589
48.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30000
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 10000
33.3%
군 934
 
3.1%
평 682
 
2.3%
신 644
 
2.1%
상 620
 
2.1%
산 618
 
2.1%
송 499
 
1.7%
양 481
 
1.6%
운 480
 
1.6%
덕 453
 
1.5%
Other values (71) 14589
48.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30000
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 10000
33.3%
군 934
 
3.1%
평 682
 
2.3%
신 644
 
2.1%
상 620
 
2.1%
산 618
 
2.1%
송 499
 
1.7%
양 481
 
1.6%
운 480
 
1.6%
덕 453
 
1.5%
Other values (71) 14589
48.6%

산여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부
9719 
여
 
281

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부 9719
97.2%
여 281
 
2.8%

Length

2024-03-15T09:20:10.163707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T09:20:10.375777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부 9719
97.2%
여 281
 
2.8%

지번본번
Real number (ℝ)

Distinct1719
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean634.593
Minimum1
Maximum2490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T09:20:10.603711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile60
Q1280
median550.5
Q3924
95-th percentile1552
Maximum2490
Range2489
Interquartile range (IQR)644

Descriptive statistics

Standard deviation454.62397
Coefficient of variation (CV)0.71640243
Kurtosis0.21693017
Mean634.593
Median Absolute Deviation (MAD)314.5
Skewness0.81874851
Sum6345930
Variance206682.95
MonotonicityNot monotonic
2024-03-15T09:20:11.050505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
406 34
 
0.3%
284 32
 
0.3%
403 29
 
0.3%
364 29
 
0.3%
32 28
 
0.3%
366 24
 
0.2%
716 24
 
0.2%
110 23
 
0.2%
79 22
 
0.2%
113 22
 
0.2%
Other values (1709) 9733
97.3%
ValueCountFrequency (%)
1 14
0.1%
2 6
0.1%
3 8
0.1%
4 3
 
< 0.1%
5 9
0.1%
6 6
0.1%
7 8
0.1%
8 7
0.1%
9 4
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
2490 1
< 0.1%
2484 1
< 0.1%
2453 1
< 0.1%
2431 1
< 0.1%
2417 1
< 0.1%
2343 1
< 0.1%
2323 2
< 0.1%
2208 1
< 0.1%
2202 1
< 0.1%
2199 1
< 0.1%

지번부번
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2614
Minimum0
Maximum463
Zeros2796
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T09:20:11.469676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile16
Maximum463
Range463
Interquartile range (IQR)4

Descriptive statistics

Standard deviation11.497517
Coefficient of variation (CV)2.6980609
Kurtosis513.07285
Mean4.2614
Median Absolute Deviation (MAD)1
Skewness16.050984
Sum42614
Variance132.19289
MonotonicityNot monotonic
2024-03-15T09:20:11.826585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2828
28.3%
0 2796
28.0%
2 795
 
8.0%
3 732
 
7.3%
4 521
 
5.2%
5 400
 
4.0%
6 279
 
2.8%
7 258
 
2.6%
8 193
 
1.9%
9 145
 
1.5%
Other values (91) 1053
 
10.5%
ValueCountFrequency (%)
0 2796
28.0%
1 2828
28.3%
2 795
 
8.0%
3 732
 
7.3%
4 521
 
5.2%
5 400
 
4.0%
6 279
 
2.8%
7 258
 
2.6%
8 193
 
1.9%
9 145
 
1.5%
ValueCountFrequency (%)
463 1
< 0.1%
452 1
< 0.1%
170 1
< 0.1%
168 1
< 0.1%
159 1
< 0.1%
144 1
< 0.1%
134 1
< 0.1%
125 1
< 0.1%
123 1
< 0.1%
115 1
< 0.1%

도로명
Text

Distinct465
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T09:20:12.814512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.4624
Min length3

Characters and Unicode

Total characters34624
Distinct characters219
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

Unique13 ?
Unique (%)0.1%

Sample

1st row동남로
2nd row오동2길
3rd row덕천로
4th row하원산길
5th row고정길
ValueCountFrequency (%)
진무로 342
 
3.4%
임진로 212
 
2.1%
진용로 154
 
1.5%
동상주천로 136
 
1.4%
진장로 126
 
1.3%
관진로 111
 
1.1%
운장로 108
 
1.1%
전진로 104
 
1.0%
안용로 98
 
1.0%
중앙로 87
 
0.9%
Other values (455) 8522
85.2%
2024-03-15T09:20:14.312051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
길 6724
 
19.4%
로 3392
 
9.8%
진 1124
 
3.2%
동 908
 
2.6%
원 685
 
2.0%
1 618
 
1.8%
2 604
 
1.7%
산 529
 
1.5%
무 481
 
1.4%
상 479
 
1.4%
Other values (209) 19080
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33232
96.0%
Decimal Number 1392
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 6724
 
20.2%
로 3392
 
10.2%
진 1124
 
3.4%
동 908
 
2.7%
원 685
 
2.1%
산 529
 
1.6%
무 481
 
1.4%
상 479
 
1.4%
장 458
 
1.4%
평 426
 
1.3%
Other values (201) 18026
54.2%
Decimal Number
ValueCountFrequency (%)
1 618
44.4%
2 604
43.4%
3 120
 
8.6%
4 44
 
3.2%
0 3
 
0.2%
5 1
 
0.1%
9 1
 
0.1%
6 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33232
96.0%
Common 1392
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 6724
 
20.2%
로 3392
 
10.2%
진 1124
 
3.4%
동 908
 
2.7%
원 685
 
2.1%
산 529
 
1.6%
무 481
 
1.4%
상 479
 
1.4%
장 458
 
1.4%
평 426
 
1.3%
Other values (201) 18026
54.2%
Common
ValueCountFrequency (%)
1 618
44.4%
2 604
43.4%
3 120
 
8.6%
4 44
 
3.2%
0 3
 
0.2%
5 1
 
0.1%
9 1
 
0.1%
6 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33232
96.0%
ASCII 1392
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
길 6724
 
20.2%
로 3392
 
10.2%
진 1124
 
3.4%
동 908
 
2.7%
원 685
 
2.1%
산 529
 
1.6%
무 481
 
1.4%
상 479
 
1.4%
장 458
 
1.4%
평 426
 
1.3%
Other values (201) 18026
54.2%
ASCII
ValueCountFrequency (%)
1 618
44.4%
2 604
43.4%
3 120
 
8.6%
4 44
 
3.2%
0 3
 
0.2%
5 1
 
0.1%
9 1
 
0.1%
6 1
 
0.1%

건물본번
Real number (ℝ)

Distinct1192
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean273.7874
Minimum1
Maximum3498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T09:20:14.773429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q118
median45
Q3177
95-th percentile1575
Maximum3498
Range3497
Interquartile range (IQR)159

Descriptive statistics

Standard deviation564.67173
Coefficient of variation (CV)2.062446
Kurtosis9.1082992
Mean273.7874
Median Absolute Deviation (MAD)35
Skewness3.003041
Sum2737874
Variance318854.17
MonotonicityNot monotonic
2024-03-15T09:20:15.251894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 195
 
1.9%
14 181
 
1.8%
7 167
 
1.7%
13 166
 
1.7%
19 162
 
1.6%
10 161
 
1.6%
15 160
 
1.6%
18 159
 
1.6%
21 157
 
1.6%
16 149
 
1.5%
Other values (1182) 8343
83.4%
ValueCountFrequency (%)
1 60
 
0.6%
2 42
 
0.4%
3 125
1.2%
4 104
1.0%
5 139
1.4%
6 195
1.9%
7 167
1.7%
8 147
1.5%
9 148
1.5%
10 161
1.6%
ValueCountFrequency (%)
3498 3
< 0.1%
3471 1
 
< 0.1%
3410 2
< 0.1%
3287 1
 
< 0.1%
3255 1
 
< 0.1%
3217 1
 
< 0.1%
3206 1
 
< 0.1%
3203 1
 
< 0.1%
3201 1
 
< 0.1%
3183 4
< 0.1%

건물부번
Real number (ℝ)

ZEROS 

Distinct174
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8266
Minimum0
Maximum513
Zeros4335
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T09:20:15.541771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310
95-th percentile44
Maximum513
Range513
Interquartile range (IQR)10

Descriptive statistics

Standard deviation22.657053
Coefficient of variation (CV)2.3056859
Kurtosis68.014428
Mean9.8266
Median Absolute Deviation (MAD)2
Skewness6.3370264
Sum98266
Variance513.34207
MonotonicityNot monotonic
2024-03-15T09:20:15.798573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4335
43.4%
1 458
 
4.6%
3 449
 
4.5%
4 430
 
4.3%
5 332
 
3.3%
6 327
 
3.3%
2 315
 
3.1%
7 286
 
2.9%
8 262
 
2.6%
9 215
 
2.1%
Other values (164) 2591
25.9%
ValueCountFrequency (%)
0 4335
43.4%
1 458
 
4.6%
2 315
 
3.1%
3 449
 
4.5%
4 430
 
4.3%
5 332
 
3.3%
6 327
 
3.3%
7 286
 
2.9%
8 262
 
2.6%
9 215
 
2.1%
ValueCountFrequency (%)
513 1
< 0.1%
373 1
< 0.1%
324 1
< 0.1%
322 1
< 0.1%
316 1
< 0.1%
280 1
< 0.1%
276 1
< 0.1%
265 1
< 0.1%
260 1
< 0.1%
251 1
< 0.1%

Interactions

2024-03-15T09:20:03.315725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:19:58.030049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:19:59.370347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:00.488602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:01.945445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:03.596891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:19:58.286053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:19:59.642933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:00.763201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:02.227398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:03.856850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:19:58.550824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:19:59.900368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:01.048297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:02.492086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:04.108421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:19:58.820427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:00.116730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:01.326959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:02.760828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:04.531983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:19:59.103848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:00.290995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:01.686698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:20:03.045971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T09:20:16.041880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번법정읍면동명법정리명산여부지번본번지번부번건물본번건물부번
연번1.0000.9190.9910.0520.4170.1510.3960.077
법정읍면동명0.9191.0001.0000.0510.3300.0880.3230.120
법정리명0.9911.0001.0000.1270.6800.1450.7500.692
산여부0.0520.0510.1271.0000.4070.0000.0660.149
지번본번0.4170.3300.6800.4071.0000.0870.2430.093
지번부번0.1510.0880.1450.0000.0871.0000.1490.032
건물본번0.3960.3230.7500.0660.2430.1491.0000.071
건물부번0.0770.1200.6920.1490.0930.0320.0711.000
2024-03-15T09:20:16.336458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정읍면동명산여부
법정읍면동명1.0000.049
산여부0.0491.000
2024-03-15T09:20:16.583624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번지번본번지번부번건물본번건물부번법정읍면동명산여부
연번1.000-0.032-0.0480.0750.0270.7200.040
지번본번-0.0321.000-0.017-0.042-0.0220.1470.313
지번부번-0.048-0.0171.0000.009-0.0390.0480.000
건물본번0.075-0.0420.0091.0000.0650.1430.050
건물부번0.027-0.022-0.0390.0651.0000.0540.149
법정읍면동명0.7200.1470.0480.1430.0541.0000.049
산여부0.0400.3130.0000.0500.1490.0491.000

Missing values

2024-03-15T09:20:04.918253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T09:20:05.252868image/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

연번시도명시군구명법정읍면동명법정리명산여부지번본번지번부번도로명건물본번건물부번
1630616307전북특별자치도진안군백운면남계리부1780동남로1450
1659616597전북특별자치도진안군마령면계서리부9252오동2길3554
1156611567전북특별자치도진안군마령면덕천리부5051덕천로9010
89378938전북특별자치도진안군백운면운교리부9231하원산길1019
67436744전북특별자치도진안군동향면신송리부7971고정길114
69176918전북특별자치도진안군동향면성산리부824하향길184
1652716528전북특별자치도진안군마령면동촌리부7037마이산남로1830
1381513816전북특별자치도진안군정천면봉학리부9850학동길600
1459314594전북특별자치도진안군주천면주양리부3833양지1길60
1637016371전북특별자치도진안군백운면동창리부1327번덕길496
연번시도명시군구명법정읍면동명법정리명산여부지번본번지번부번도로명건물본번건물부번
47634764전북특별자치도진안군용담면송풍리부5681노온길346
59525953전북특별자치도진안군동향면대량리부7001상양지길350
78677868전북특별자치도진안군백운면평장리부5240임진로16759
1684816849전북특별자치도진안군주천면주양리부750감나무골길800
39513952전북특별자치도진안군진안읍운산리부15932구운길399
69966997전북특별자치도진안군상전면주평리부14701문화길236
1417114172전북특별자치도진안군정천면모정리부20380진용로1405213
20822083전북특별자치도진안군진안읍반월리부9673진무로6650
18821883전북특별자치도진안군진안읍가림리부9851은천3길105
14681469전북특별자치도진안군진안읍단양리부7810마이산로1540