Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows46
Duplicate rows (%)0.5%
Total size in memory1.1 MiB
Average record size in memory114.0 B

Variable types

Categorical3
Text1
Numeric8

Dataset

Description지자체별 토지대장 발급 등 민원처리상황에 대한 통계자료입니다.행정구역(시군구)별 민원처리종목 기준으로 누계 지번 수,누계 발급 수,누계 수수료,유료 지번 수,유료 건수,유료 수수료,무료 지번 수,무료 발급 수,무료 수수료 항목에 대한 통계자료를 제공합니다.
Author국토교통부
URLhttps://www.data.go.kr/data/15063994/fileData.do

Alerts

통계 기준년도 has constant value ""Constant
무료_수수료 has constant value ""Constant
Dataset has 46 (0.5%) duplicate rowsDuplicates
누계_지번_수 is highly overall correlated with 누계_발급_수 and 6 other fieldsHigh correlation
누계_발급_수 is highly overall correlated with 누계_지번_수 and 6 other fieldsHigh correlation
누계_수수료 is highly overall correlated with 누계_지번_수 and 4 other fieldsHigh correlation
유료_지번_수 is highly overall correlated with 누계_지번_수 and 4 other fieldsHigh correlation
유료_건수 is highly overall correlated with 누계_지번_수 and 4 other fieldsHigh correlation
유료_수수료 is highly overall correlated with 누계_지번_수 and 4 other fieldsHigh correlation
무료_지번_수 is highly overall correlated with 누계_지번_수 and 2 other fieldsHigh correlation
무료_발급_수 is highly overall correlated with 누계_지번_수 and 2 other fieldsHigh correlation
누계_지번_수 has 523 (5.2%) zerosZeros
누계_발급_수 has 523 (5.2%) zerosZeros
누계_수수료 has 2387 (23.9%) zerosZeros
유료_지번_수 has 1640 (16.4%) zerosZeros
유료_건수 has 1745 (17.4%) zerosZeros
유료_수수료 has 2275 (22.8%) zerosZeros
무료_지번_수 has 4385 (43.9%) zerosZeros
무료_발급_수 has 4381 (43.8%) zerosZeros

Reproduction

Analysis started2023-12-12 10:21:29.764628
Analysis finished2023-12-12 10:21:41.261688
Duration11.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계 기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 10000
100.0%

Length

2023-12-12T19:21:41.335526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:21:41.421118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10000
100.0%

행정구역
Text

Distinct259
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T19:21:41.767328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length8
Mean length8.5198
Min length7

Characters and Unicode

Total characters85198
Distinct characters149
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

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 부산진구
2nd row전라북도 임실군
3rd row부산광역시 서구
4th row전라남도 목포시
5th row전라남도 완도군
ValueCountFrequency (%)
경기도 1741
 
8.2%
서울특별시 969
 
4.6%
경상북도 946
 
4.5%
전라남도 897
 
4.2%
경상남도 873
 
4.1%
강원도 701
 
3.3%
부산광역시 668
 
3.2%
전라북도 621
 
2.9%
충청남도 616
 
2.9%
충청북도 525
 
2.5%
Other values (243) 12632
59.6%
2023-12-12T19:21:42.395146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11189
 
13.1%
도 7204
 
8.5%
시 7071
 
8.3%
구 4517
 
5.3%
경 3696
 
4.3%
군 3285
 
3.9%
남 3063
 
3.6%
광 2425
 
2.8%
북 2424
 
2.8%
역 1992
 
2.3%
Other values (139) 38332
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 74009
86.9%
Space Separator 11189
 
13.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 7204
 
9.7%
시 7071
 
9.6%
구 4517
 
6.1%
경 3696
 
5.0%
군 3285
 
4.4%
남 3063
 
4.1%
광 2425
 
3.3%
북 2424
 
3.3%
역 1992
 
2.7%
상 1967
 
2.7%
Other values (138) 36365
49.1%
Space Separator
ValueCountFrequency (%)
11189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 74009
86.9%
Common 11189
 
13.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 7204
 
9.7%
시 7071
 
9.6%
구 4517
 
6.1%
경 3696
 
5.0%
군 3285
 
4.4%
남 3063
 
4.1%
광 2425
 
3.3%
북 2424
 
3.3%
역 1992
 
2.7%
상 1967
 
2.7%
Other values (138) 36365
49.1%
Common
ValueCountFrequency (%)
11189
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 74009
86.9%
ASCII 11189
 
13.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11189
100.0%
Hangul
ValueCountFrequency (%)
도 7204
 
9.7%
시 7071
 
9.6%
구 4517
 
6.1%
경 3696
 
5.0%
군 3285
 
4.4%
남 3063
 
4.1%
광 2425
 
3.3%
북 2424
 
3.3%
역 1992
 
2.7%
상 1967
 
2.7%
Other values (138) 36365
49.1%

토지_이동_종목
Categorical

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
3354 
분할개시 결정
 
216
해면성복구
 
215
번과 합병
 
215
경계정정
 
212
Other values (43)
5788 

Length

Max length19
Median length17
Mean length7.4015
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row등록사항 회복 ( )
4th row번과 합병되어 말소
5th row에서 지번변경

Common Values

ValueCountFrequency (%)
<NA> 3354
33.5%
분할개시 결정 216
 
2.2%
해면성복구 215
 
2.1%
번과 합병 215
 
2.1%
경계정정 212
 
2.1%
면적정정 211
 
2.1%
등록사항 말소 ( ) 210
 
2.1%
번에서 분할 208
 
2.1%
분할되어 본번에 을 부함 208
 
2.1%
구획정리 시행신고 207
 
2.1%
Other values (38) 4744
47.4%

Length

2023-12-12T19:21:42.560087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3354
 
18.3%
1230
 
6.7%
경지정리 672
 
3.7%
지적재조사 652
 
3.6%
말소 617
 
3.4%
폐쇄 587
 
3.2%
시행신고폐지 570
 
3.1%
시행신고 480
 
2.6%
완료 442
 
2.4%
구획정리 435
 
2.4%
Other values (49) 9240
50.5%

누계_지번_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2755
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7743.7111
Minimum0
Maximum562130
Zeros523
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:21:42.698262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median60
Q3550
95-th percentile36656.8
Maximum562130
Range562130
Interquartile range (IQR)543

Descriptive statistics

Standard deviation32435.316
Coefficient of variation (CV)4.188601
Kurtosis58.702587
Mean7743.7111
Median Absolute Deviation (MAD)59
Skewness6.7925127
Sum77437111
Variance1.0520497 × 109
MonotonicityNot monotonic
2023-12-12T19:21:42.859290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 561
 
5.6%
0 523
 
5.2%
2 367
 
3.7%
3 296
 
3.0%
4 232
 
2.3%
5 203
 
2.0%
6 185
 
1.8%
7 176
 
1.8%
9 139
 
1.4%
8 123
 
1.2%
Other values (2745) 7195
72.0%
ValueCountFrequency (%)
0 523
5.2%
1 561
5.6%
2 367
3.7%
3 296
3.0%
4 232
2.3%
5 203
 
2.0%
6 185
 
1.8%
7 176
 
1.8%
8 123
 
1.2%
9 139
 
1.4%
ValueCountFrequency (%)
562130 1
< 0.1%
525688 1
< 0.1%
430278 1
< 0.1%
424421 1
< 0.1%
419309 1
< 0.1%
412663 1
< 0.1%
395355 1
< 0.1%
362662 1
< 0.1%
358217 1
< 0.1%
328527 1
< 0.1%

누계_발급_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2773
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7760.5276
Minimum0
Maximum563132
Zeros523
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:21:43.010006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median60
Q3557.5
95-th percentile36742.3
Maximum563132
Range563132
Interquartile range (IQR)550.5

Descriptive statistics

Standard deviation32466.05
Coefficient of variation (CV)4.1834849
Kurtosis58.653324
Mean7760.5276
Median Absolute Deviation (MAD)59
Skewness6.7888196
Sum77605276
Variance1.0540444 × 109
MonotonicityNot monotonic
2023-12-12T19:21:43.156708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 559
 
5.6%
0 523
 
5.2%
2 368
 
3.7%
3 297
 
3.0%
4 229
 
2.3%
5 202
 
2.0%
6 183
 
1.8%
7 179
 
1.8%
9 139
 
1.4%
8 123
 
1.2%
Other values (2763) 7198
72.0%
ValueCountFrequency (%)
0 523
5.2%
1 559
5.6%
2 368
3.7%
3 297
3.0%
4 229
2.3%
5 202
 
2.0%
6 183
 
1.8%
7 179
 
1.8%
8 123
 
1.2%
9 139
 
1.4%
ValueCountFrequency (%)
563132 1
< 0.1%
525715 1
< 0.1%
430279 1
< 0.1%
424421 1
< 0.1%
420204 1
< 0.1%
412692 1
< 0.1%
395357 1
< 0.1%
362958 1
< 0.1%
359063 1
< 0.1%
328532 1
< 0.1%

누계_수수료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3004
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean494312.4
Minimum0
Maximum93115000
Zeros2387
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:21:43.356792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1300
median11500
Q3153650
95-th percentile2391905
Maximum93115000
Range93115000
Interquartile range (IQR)153350

Descriptive statistics

Standard deviation2213652.3
Coefficient of variation (CV)4.4782455
Kurtosis404.0848
Mean494312.4
Median Absolute Deviation (MAD)11500
Skewness14.860899
Sum4.943124 × 109
Variance4.9002563 × 1012
MonotonicityNot monotonic
2023-12-12T19:21:43.504119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2387
 
23.9%
300 200
 
2.0%
500 155
 
1.6%
800 130
 
1.3%
2000 104
 
1.0%
1200 100
 
1.0%
600 94
 
0.9%
1000 89
 
0.9%
4000 81
 
0.8%
1500 78
 
0.8%
Other values (2994) 6582
65.8%
ValueCountFrequency (%)
0 2387
23.9%
200 2
 
< 0.1%
300 200
 
2.0%
400 77
 
0.8%
500 155
 
1.6%
600 94
 
0.9%
700 2
 
< 0.1%
800 130
 
1.3%
900 58
 
0.6%
901 1
 
< 0.1%
ValueCountFrequency (%)
93115000 1
< 0.1%
43966000 1
< 0.1%
39758500 1
< 0.1%
39608000 1
< 0.1%
39427000 1
< 0.1%
38340000 1
< 0.1%
37262000 1
< 0.1%
35396000 1
< 0.1%
30902000 1
< 0.1%
29547000 1
< 0.1%

유료_지번_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1847
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean692.9665
Minimum0
Maximum94525
Zeros1640
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:21:43.641945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median24
Q3245.25
95-th percentile3625.6
Maximum94525
Range94525
Interquartile range (IQR)243.25

Descriptive statistics

Standard deviation2733.8659
Coefficient of variation (CV)3.9451632
Kurtosis203.966
Mean692.9665
Median Absolute Deviation (MAD)24
Skewness10.559045
Sum6929665
Variance7474022.8
MonotonicityNot monotonic
2023-12-12T19:21:43.777009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1640
 
16.4%
1 602
 
6.0%
2 382
 
3.8%
3 294
 
2.9%
4 245
 
2.5%
5 195
 
1.9%
6 170
 
1.7%
7 168
 
1.7%
8 133
 
1.3%
9 129
 
1.3%
Other values (1837) 6042
60.4%
ValueCountFrequency (%)
0 1640
16.4%
1 602
 
6.0%
2 382
 
3.8%
3 294
 
2.9%
4 245
 
2.5%
5 195
 
1.9%
6 170
 
1.7%
7 168
 
1.7%
8 133
 
1.3%
9 129
 
1.3%
ValueCountFrequency (%)
94525 1
< 0.1%
47674 1
< 0.1%
45365 1
< 0.1%
40509 1
< 0.1%
40118 1
< 0.1%
39252 1
< 0.1%
38217 1
< 0.1%
36935 1
< 0.1%
36256 1
< 0.1%
34454 1
< 0.1%

유료_건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1819
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean678.1917
Minimum0
Maximum94525
Zeros1745
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:21:43.927052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median23
Q3233
95-th percentile3513.8
Maximum94525
Range94525
Interquartile range (IQR)231

Descriptive statistics

Standard deviation2710.6617
Coefficient of variation (CV)3.996896
Kurtosis209.87161
Mean678.1917
Median Absolute Deviation (MAD)23
Skewness10.714871
Sum6781917
Variance7347687
MonotonicityNot monotonic
2023-12-12T19:21:44.097211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1745
 
17.4%
1 599
 
6.0%
2 383
 
3.8%
3 293
 
2.9%
4 242
 
2.4%
5 193
 
1.9%
6 167
 
1.7%
7 166
 
1.7%
8 131
 
1.3%
9 128
 
1.3%
Other values (1809) 5953
59.5%
ValueCountFrequency (%)
0 1745
17.4%
1 599
 
6.0%
2 383
 
3.8%
3 293
 
2.9%
4 242
 
2.4%
5 193
 
1.9%
6 167
 
1.7%
7 166
 
1.7%
8 131
 
1.3%
9 128
 
1.3%
ValueCountFrequency (%)
94525 1
< 0.1%
46997 1
< 0.1%
45365 1
< 0.1%
40509 1
< 0.1%
40118 1
< 0.1%
39252 1
< 0.1%
38217 1
< 0.1%
36935 1
< 0.1%
36256 1
< 0.1%
33529 1
< 0.1%

유료_수수료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3063
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499971.34
Minimum0
Maximum93115000
Zeros2275
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:21:44.242247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1400
median12600
Q3160800
95-th percentile2416645
Maximum93115000
Range93115000
Interquartile range (IQR)160400

Descriptive statistics

Standard deviation2216713.6
Coefficient of variation (CV)4.4336814
Kurtosis401.72214
Mean499971.34
Median Absolute Deviation (MAD)12600
Skewness14.801397
Sum4.9997134 × 109
Variance4.9138193 × 1012
MonotonicityNot monotonic
2023-12-12T19:21:44.411333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2275
 
22.8%
300 200
 
2.0%
500 157
 
1.6%
800 130
 
1.3%
2000 102
 
1.0%
1200 100
 
1.0%
600 94
 
0.9%
1000 91
 
0.9%
4000 84
 
0.8%
1500 79
 
0.8%
Other values (3053) 6688
66.9%
ValueCountFrequency (%)
0 2275
22.8%
200 2
 
< 0.1%
300 200
 
2.0%
400 77
 
0.8%
500 157
 
1.6%
600 94
 
0.9%
700 3
 
< 0.1%
800 130
 
1.3%
900 59
 
0.6%
901 1
 
< 0.1%
ValueCountFrequency (%)
93115000 1
< 0.1%
43966000 1
< 0.1%
39758500 1
< 0.1%
39608000 1
< 0.1%
39427000 1
< 0.1%
38340000 1
< 0.1%
37262000 1
< 0.1%
35396000 1
< 0.1%
30902000 1
< 0.1%
29547000 1
< 0.1%

무료_지번_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1948
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7060.3313
Minimum0
Maximum527648
Zeros4385
Zeros (%)43.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:21:44.552270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q354
95-th percentile34756.4
Maximum527648
Range527648
Interquartile range (IQR)54

Descriptive statistics

Standard deviation31454.042
Coefficient of variation (CV)4.4550377
Kurtosis59.959923
Mean7060.3313
Median Absolute Deviation (MAD)2
Skewness6.8973472
Sum70603313
Variance9.8935677 × 108
MonotonicityNot monotonic
2023-12-12T19:21:44.707496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4385
43.9%
1 514
 
5.1%
2 299
 
3.0%
3 240
 
2.4%
4 214
 
2.1%
5 182
 
1.8%
6 159
 
1.6%
9 110
 
1.1%
7 109
 
1.1%
8 106
 
1.1%
Other values (1938) 3682
36.8%
ValueCountFrequency (%)
0 4385
43.9%
1 514
 
5.1%
2 299
 
3.0%
3 240
 
2.4%
4 214
 
2.1%
5 182
 
1.8%
6 159
 
1.6%
7 109
 
1.1%
8 106
 
1.1%
9 110
 
1.1%
ValueCountFrequency (%)
527648 1
< 0.1%
521712 1
< 0.1%
429630 1
< 0.1%
424355 1
< 0.1%
409063 1
< 0.1%
395082 1
< 0.1%
371602 1
< 0.1%
348160 1
< 0.1%
330309 1
< 0.1%
328374 1
< 0.1%

무료_발급_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1960
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7064.0703
Minimum0
Maximum527786
Zeros4381
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:21:44.841949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q355
95-th percentile34760.2
Maximum527786
Range527786
Interquartile range (IQR)55

Descriptive statistics

Standard deviation31461.412
Coefficient of variation (CV)4.453723
Kurtosis59.932573
Mean7064.0703
Median Absolute Deviation (MAD)2
Skewness6.8957319
Sum70640703
Variance9.8982046 × 108
MonotonicityNot monotonic
2023-12-12T19:21:44.970670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4381
43.8%
1 512
 
5.1%
2 298
 
3.0%
3 239
 
2.4%
4 217
 
2.2%
5 180
 
1.8%
6 156
 
1.6%
7 115
 
1.1%
9 110
 
1.1%
8 105
 
1.1%
Other values (1950) 3687
36.9%
ValueCountFrequency (%)
0 4381
43.8%
1 512
 
5.1%
2 298
 
3.0%
3 239
 
2.4%
4 217
 
2.2%
5 180
 
1.8%
6 156
 
1.6%
7 115
 
1.1%
8 105
 
1.1%
9 110
 
1.1%
ValueCountFrequency (%)
527786 1
< 0.1%
521712 1
< 0.1%
429630 1
< 0.1%
424355 1
< 0.1%
409063 1
< 0.1%
395082 1
< 0.1%
371737 1
< 0.1%
348296 1
< 0.1%
330402 1
< 0.1%
328374 1
< 0.1%

무료_수수료
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-12-12T19:21:45.092666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:21:45.178223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

Interactions

2023-12-12T19:21:39.834242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:33.133376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:34.244566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:35.214167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:36.203412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:37.112710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:37.975164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:38.821596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:39.946333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:33.293051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:34.371886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:35.339752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:36.339294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:37.209941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:38.081733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:38.926869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:40.042693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:33.452822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:34.504132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:35.479553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:36.440178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:37.318895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:38.191662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:39.054658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:40.166541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:33.561479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:34.612328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:35.581440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:36.550834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:37.438962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:38.289434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:39.201845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:40.571098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:33.671775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:34.735068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:35.694928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:36.679322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:37.528413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:38.384017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:39.377374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:40.670496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:33.799940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:34.857394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:35.819896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:36.795751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:37.639807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:38.498938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:39.502581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:40.758881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:33.934746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:34.970435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:35.939932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:36.920520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:37.762223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:38.616542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:39.615358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:40.854638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:34.076738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:35.090963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:36.070134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:37.015795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:37.859609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:38.719636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:21:39.729055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:21:45.245333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
토지_이동_종목누계_지번_수누계_발급_수누계_수수료유료_지번_수유료_건수유료_수수료무료_지번_수무료_발급_수
토지_이동_종목1.0000.6950.6950.4510.5170.5310.4510.6910.692
누계_지번_수0.6951.0001.0000.2530.4700.4370.2530.9610.963
누계_발급_수0.6951.0001.0000.2530.4730.4420.2530.9580.959
누계_수수료0.4510.2530.2531.0000.9060.9791.0000.2230.224
유료_지번_수0.5170.4700.4730.9061.0000.9930.9060.5230.523
유료_건수0.5310.4370.4420.9790.9931.0000.9790.4030.404
유료_수수료0.4510.2530.2531.0000.9060.9791.0000.2230.224
무료_지번_수0.6910.9610.9580.2230.5230.4030.2231.0001.000
무료_발급_수0.6920.9630.9590.2240.5230.4040.2241.0001.000
2023-12-12T19:21:45.427284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
누계_지번_수누계_발급_수누계_수수료유료_지번_수유료_건수유료_수수료무료_지번_수무료_발급_수토지_이동_종목
누계_지번_수1.0001.0000.6990.6860.7150.6690.7730.7720.331
누계_발급_수1.0001.0000.7000.6870.7150.6700.7730.7720.331
누계_수수료0.6990.7001.0000.9400.9630.9760.3330.3330.210
유료_지번_수0.6860.6870.9401.0000.9740.9590.3220.3210.236
유료_건수0.7150.7150.9630.9741.0000.9370.3200.3200.257
유료_수수료0.6690.6700.9760.9590.9371.0000.3290.3310.210
무료_지번_수0.7730.7730.3330.3220.3200.3291.0000.9990.314
무료_발급_수0.7720.7720.3330.3210.3200.3310.9991.0000.315
토지_이동_종목0.3310.3310.2100.2360.2570.2100.3140.3151.000

Missing values

2023-12-12T19:21:41.007284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:21:41.176947image/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

통계 기준년도행정구역토지_이동_종목누계_지번_수누계_발급_수누계_수수료유료_지번_수유료_건수유료_수수료무료_지번_수무료_발급_수무료_수수료
12722022부산광역시 부산진구<NA>000000000
79242022전라북도 임실군<NA>440440000
11712022부산광역시 서구등록사항 회복 ( )174174104000173173104000110
80502022전라남도 목포시번과 합병되어 말소606023200585823200220
89122022전라남도 완도군에서 지번변경000535005355330
47302022경기도 용인시 수지구축척변경 되어 폐쇄1471476900014514569000220
30962022울산광역시 중구구획정리 시행신고폐지880880000
28912022대전광역시 중구<NA>896908448900887882448900880
74052022전라북도 전주시 완산구<NA>309309245600307307245600220
10142022서울특별시 송파구번으로 등록전환되어 말소75175751755630068595630075107751070
통계 기준년도행정구역토지_이동_종목누계_지번_수누계_발급_수누계_수수료유료_지번_수유료_건수유료_수수료무료_지번_수무료_발급_수무료_수수료
11572022부산광역시 서구지적재조사로 폐쇄111111000111111000000
28432022광주광역시 광산구<NA>1171179040011311390400440
6352022서울특별시 양천구에서 행정구역명칭변경102102200044200098980
43922022경기도 오산시지적재조사로 폐쇄222000222000000
66412022충청북도 단양군번과 합병되어 말소393915200383815200110
44102022경기도 오산시<NA>0003117015610003323320
72662022충청남도 홍성군등록사항 말소 ( )283283140000282282140000110
53542022강원도 원주시<NA>42242200004224220
97962022경상북도 청도군축척변경 되어 폐쇄330000330
81412022전라남도 여수시공유지연명부 등록사항정정 ( )000000000

Duplicate rows

Most frequently occurring

통계 기준년도행정구역토지_이동_종목누계_지번_수누계_발급_수누계_수수료유료_지번_수유료_건수유료_수수료무료_지번_수무료_발급_수무료_수수료# duplicates
52022강원도 원주시<NA>00000000019
22022강원도 양구군<NA>00000000016
02022강원도 강릉시<NA>00000000014
82022강원도 인제군<NA>00000000014
362022전라북도 남원시<NA>00000000013
202022경상북도 경산시<NA>00000000010
232022경상북도 영양군<NA>00000000010
242022대구광역시 달성군<NA>00000000010
342022전라남도 진도군<NA>00000000010
442022충청북도 음성군<NA>00000000010