Overview

Dataset statistics

Number of variables28
Number of observations10000
Missing cells38035
Missing cells (%)13.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory256.0 B

Variable types

Categorical8
Text4
Numeric14
Unsupported2

Dataset

Description상태 (공통),지주관리번호,형식코드,규격코드,고가 (공통),용융도금,교차로코드,설치일,교체일,구경찰서코드 (공통),구코드 (공통),관할여부,동코드 (공통),지번,비고,신경찰서코드 (공통),작업구분 (공통),표출구분 (공통),설치방식,도로구분 (공통),관할사업소 (공통),공간데이터,신규정규화ID,참조코드,이력ID,공사관리번호,지주관리번호,공사형태 (공통)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15539/S/1/datasetView.do

Alerts

상태 (공통) is highly imbalanced (99.0%)Imbalance
고가 (공통) is highly imbalanced (94.7%)Imbalance
설치방식 is highly imbalanced (91.7%)Imbalance
교차로코드 has 240 (2.4%) missing valuesMissing
구경찰서코드 (공통) has 121 (1.2%) missing valuesMissing
구코드 (공통) has 123 (1.2%) missing valuesMissing
지번 has 481 (4.8%) missing valuesMissing
비고 has 10000 (100.0%) missing valuesMissing
신경찰서코드 (공통) has 106 (1.1%) missing valuesMissing
공간데이터 has 10000 (100.0%) missing valuesMissing
신규정규화ID has 7571 (75.7%) missing valuesMissing
참조코드 has 6434 (64.3%) missing valuesMissing
공사관리번호 has 285 (2.9%) missing valuesMissing
공사형태 (공통) has 2590 (25.9%) missing valuesMissing
이력ID has unique valuesUnique
비고 is an unsupported type, check if it needs cleaning or further analysisUnsupported
공간데이터 is an unsupported type, check if it needs cleaning or further analysisUnsupported
교차로코드 has 7245 (72.5%) zerosZeros
동코드 (공통) has 320 (3.2%) zerosZeros

Reproduction

Analysis started2024-05-04 04:23:07.298464
Analysis finished2024-05-04 04:23:08.998260
Duration1.7 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상태 (공통)
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9983 
2
 
9
3
 
5
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9983
99.8%
2 9
 
0.1%
3 5
 
0.1%
4 3
 
< 0.1%

Length

2024-05-04T04:23:09.418285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T04:23:09.752168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9983
99.8%
2 9
 
0.1%
3 5
 
< 0.1%
4 3
 
< 0.1%

지주관리번호
Text

Distinct9919
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T04:23:10.275631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters130000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9838 ?
Unique (%)98.4%

Sample

1st row02-0000012385
2nd row02-0000014383
3rd row02-0000010174
4th row02-0000014409
5th row02-0000180282
ValueCountFrequency (%)
02-0000013047 2
 
< 0.1%
02-0000029836 2
 
< 0.1%
02-0000108713 2
 
< 0.1%
02-0000010399 2
 
< 0.1%
02-0000003301 2
 
< 0.1%
02-0000001333 2
 
< 0.1%
02-0000026562 2
 
< 0.1%
02-0000006437 2
 
< 0.1%
02-0000095884 2
 
< 0.1%
02-0000016652 2
 
< 0.1%
Other values (9909) 9980
99.8%
2024-05-04T04:23:11.413230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 64448
49.6%
2 15577
 
12.0%
- 10000
 
7.7%
1 7981
 
6.1%
3 5452
 
4.2%
9 4664
 
3.6%
4 4581
 
3.5%
5 4541
 
3.5%
7 4458
 
3.4%
6 4172
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120000
92.3%
Dash Punctuation 10000
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64448
53.7%
2 15577
 
13.0%
1 7981
 
6.7%
3 5452
 
4.5%
9 4664
 
3.9%
4 4581
 
3.8%
5 4541
 
3.8%
7 4458
 
3.7%
6 4172
 
3.5%
8 4126
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64448
49.6%
2 15577
 
12.0%
- 10000
 
7.7%
1 7981
 
6.1%
3 5452
 
4.2%
9 4664
 
3.6%
4 4581
 
3.5%
5 4541
 
3.5%
7 4458
 
3.4%
6 4172
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64448
49.6%
2 15577
 
12.0%
- 10000
 
7.7%
1 7981
 
6.1%
3 5452
 
4.2%
9 4664
 
3.6%
4 4581
 
3.5%
5 4541
 
3.5%
7 4458
 
3.4%
6 4172
 
3.2%

형식코드
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.9842
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:23:11.960684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q38
95-th percentile8
Maximum999
Range998
Interquartile range (IQR)7

Descriptive statistics

Standard deviation88.053886
Coefficient of variation (CV)6.7816181
Kurtosis121.29963
Mean12.9842
Median Absolute Deviation (MAD)1
Skewness11.094833
Sum129842
Variance7753.4869
MonotonicityNot monotonic
2024-05-04T04:23:12.522304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 3679
36.8%
8 3031
30.3%
7 2563
25.6%
6 316
 
3.2%
5 117
 
1.2%
999 79
 
0.8%
15 58
 
0.6%
11 58
 
0.6%
10 41
 
0.4%
3 13
 
0.1%
Other values (7) 45
 
0.4%
ValueCountFrequency (%)
1 3679
36.8%
2 1
 
< 0.1%
3 13
 
0.1%
4 3
 
< 0.1%
5 117
 
1.2%
6 316
 
3.2%
7 2563
25.6%
8 3031
30.3%
9 2
 
< 0.1%
10 41
 
0.4%
ValueCountFrequency (%)
999 79
 
0.8%
17 13
 
0.1%
16 10
 
0.1%
15 58
 
0.6%
14 5
 
0.1%
12 11
 
0.1%
11 58
 
0.6%
10 41
 
0.4%
9 2
 
< 0.1%
8 3031
30.3%

규격코드
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7.2568541
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:23:13.059183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median10
Q310
95-th percentile10
Maximum17
Range16
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4553036
Coefficient of variation (CV)0.47614346
Kurtosis-0.96205705
Mean7.2568541
Median Absolute Deviation (MAD)0
Skewness-0.64140933
Sum72525
Variance11.939123
MonotonicityNot monotonic
2024-05-04T04:23:13.721231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
10 5190
51.9%
6 1140
 
11.4%
1 1053
 
10.5%
2 704
 
7.0%
4 633
 
6.3%
7 421
 
4.2%
8 355
 
3.5%
3 301
 
3.0%
13 93
 
0.9%
5 53
 
0.5%
Other values (4) 51
 
0.5%
ValueCountFrequency (%)
1 1053
 
10.5%
2 704
 
7.0%
3 301
 
3.0%
4 633
 
6.3%
5 53
 
0.5%
6 1140
 
11.4%
7 421
 
4.2%
8 355
 
3.5%
9 27
 
0.3%
10 5190
51.9%
ValueCountFrequency (%)
17 20
 
0.2%
13 93
 
0.9%
12 1
 
< 0.1%
11 3
 
< 0.1%
10 5190
51.9%
9 27
 
0.3%
8 355
 
3.5%
7 421
 
4.2%
6 1140
 
11.4%
5 53
 
0.5%

고가 (공통)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9897 
2
 
102
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9897
99.0%
2 102
 
1.0%
3 1
 
< 0.1%

Length

2024-05-04T04:23:14.326782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T04:23:14.757511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9897
99.0%
2 102
 
1.0%
3 1
 
< 0.1%

용융도금
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
7876 
2
2124 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 7876
78.8%
2 2124
 
21.2%

Length

2024-05-04T04:23:15.286204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T04:23:15.617579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7876
78.8%
2 2124
 
21.2%

교차로코드
Real number (ℝ)

MISSING  ZEROS 

Distinct1631
Distinct (%)16.7%
Missing240
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean563.98801
Minimum0
Maximum8810
Zeros7245
Zeros (%)72.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:23:16.039367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3113.5
95-th percentile3843.15
Maximum8810
Range8810
Interquartile range (IQR)113.5

Descriptive statistics

Standard deviation1342.2473
Coefficient of variation (CV)2.3799217
Kurtosis8.7943194
Mean563.98801
Median Absolute Deviation (MAD)0
Skewness2.9330053
Sum5504523
Variance1801627.8
MonotonicityNot monotonic
2024-05-04T04:23:16.501888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7245
72.5%
1444 8
 
0.1%
194 7
 
0.1%
577 7
 
0.1%
1384 6
 
0.1%
3459 6
 
0.1%
644 6
 
0.1%
3441 6
 
0.1%
133 6
 
0.1%
2887 6
 
0.1%
Other values (1621) 2457
 
24.6%
(Missing) 240
 
2.4%
ValueCountFrequency (%)
0 7245
72.5%
5 1
 
< 0.1%
6 2
 
< 0.1%
28 2
 
< 0.1%
29 1
 
< 0.1%
32 3
 
< 0.1%
34 3
 
< 0.1%
38 2
 
< 0.1%
39 2
 
< 0.1%
41 1
 
< 0.1%
ValueCountFrequency (%)
8810 1
< 0.1%
8787 1
< 0.1%
8751 1
< 0.1%
8518 1
< 0.1%
8448 1
< 0.1%
8432 1
< 0.1%
8363 1
< 0.1%
8294 1
< 0.1%
8251 1
< 0.1%
8237 1
< 0.1%

설치일
Real number (ℝ)

Distinct611
Distinct (%)6.1%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean15294337
Minimum10101
Maximum20240408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:23:16.966900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10101
5-th percentile10101
Q120000101
median20000101
Q320000101
95-th percentile20131127
Maximum20240408
Range20230307
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8497860.6
Coefficient of variation (CV)0.55562137
Kurtosis-0.45547258
Mean15294337
Median Absolute Deviation (MAD)0
Skewness-1.2426994
Sum1.5275984 × 1011
Variance7.2213635 × 1013
MonotonicityNot monotonic
2024-05-04T04:23:17.603503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000101 6028
60.3%
10101 2358
 
23.6%
20010101 83
 
0.8%
20041120 76
 
0.8%
20051230 31
 
0.3%
20040630 24
 
0.2%
20031231 21
 
0.2%
20050614 20
 
0.2%
20030331 20
 
0.2%
20051020 19
 
0.2%
Other values (601) 1308
 
13.1%
ValueCountFrequency (%)
10101 2358
23.6%
10091203 1
 
< 0.1%
19840101 1
 
< 0.1%
19890101 1
 
< 0.1%
19900101 1
 
< 0.1%
19910101 2
 
< 0.1%
19920101 1
 
< 0.1%
19930101 1
 
< 0.1%
19940101 2
 
< 0.1%
19960101 2
 
< 0.1%
ValueCountFrequency (%)
20240408 2
 
< 0.1%
20240214 1
 
< 0.1%
20240115 1
 
< 0.1%
20231218 1
 
< 0.1%
20231130 2
 
< 0.1%
20231129 8
0.1%
20230830 1
 
< 0.1%
20230620 1
 
< 0.1%
20230531 1
 
< 0.1%
20230328 1
 
< 0.1%

교체일
Real number (ℝ)

Distinct789
Distinct (%)7.9%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean17322388
Minimum10101
Maximum91071201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:23:18.198855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10101
5-th percentile10101
Q120000101
median20000101
Q320000101
95-th percentile20131204
Maximum91071201
Range91061100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6875578.9
Coefficient of variation (CV)0.39691865
Kurtosis3.7744158
Mean17322388
Median Absolute Deviation (MAD)0
Skewness-1.9795241
Sum1.7301601 × 1011
Variance4.7273585 × 1013
MonotonicityNot monotonic
2024-05-04T04:23:18.807046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000101 5890
58.9%
10101 1348
 
13.5%
20010101 188
 
1.9%
20041120 93
 
0.9%
20040630 52
 
0.5%
19940101 48
 
0.5%
19930101 38
 
0.4%
20020101 36
 
0.4%
20051230 36
 
0.4%
19950101 33
 
0.3%
Other values (779) 2226
 
22.3%
ValueCountFrequency (%)
10101 1348
13.5%
19710101 2
 
< 0.1%
19800102 3
 
< 0.1%
19810101 2
 
< 0.1%
19820101 2
 
< 0.1%
19830601 2
 
< 0.1%
19830801 1
 
< 0.1%
19840101 2
 
< 0.1%
19850101 9
 
0.1%
19851101 3
 
< 0.1%
ValueCountFrequency (%)
91071201 1
 
< 0.1%
20240408 2
 
< 0.1%
20240214 1
 
< 0.1%
20240115 1
 
< 0.1%
20231218 1
 
< 0.1%
20231130 2
 
< 0.1%
20231129 8
0.1%
20231110 1
 
< 0.1%
20230830 1
 
< 0.1%
20230620 1
 
< 0.1%

구경찰서코드 (공통)
Real number (ℝ)

MISSING 

Distinct31
Distinct (%)0.3%
Missing121
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean263.7848
Minimum110
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:23:19.331146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile120
Q1210
median260
Q3340
95-th percentile410
Maximum410
Range300
Interquartile range (IQR)130

Descriptive statistics

Standard deviation87.10083
Coefficient of variation (CV)0.33019655
Kurtosis-1.0665172
Mean263.7848
Median Absolute Deviation (MAD)70
Skewness-0.080539564
Sum2605930
Variance7586.5546
MonotonicityNot monotonic
2024-05-04T04:23:19.820577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
210 783
 
7.8%
340 683
 
6.8%
290 667
 
6.7%
330 639
 
6.4%
140 611
 
6.1%
260 594
 
5.9%
280 584
 
5.8%
220 567
 
5.7%
410 515
 
5.1%
240 508
 
5.1%
Other values (21) 3728
37.3%
ValueCountFrequency (%)
110 258
2.6%
120 405
4.0%
130 185
 
1.8%
140 611
6.1%
150 45
 
0.4%
160 127
 
1.3%
170 505
5.1%
180 65
 
0.7%
190 81
 
0.8%
200 93
 
0.9%
ValueCountFrequency (%)
410 515
5.1%
400 59
 
0.6%
390 412
4.1%
380 249
 
2.5%
370 83
 
0.8%
360 485
4.9%
350 125
 
1.2%
340 683
6.8%
330 639
6.4%
320 39
 
0.4%

구코드 (공통)
Real number (ℝ)

MISSING 

Distinct25
Distinct (%)0.3%
Missing123
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean492.92093
Minimum110
Maximum740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:23:20.287692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile140
Q1380
median540
Q3650
95-th percentile710
Maximum740
Range630
Interquartile range (IQR)270

Descriptive statistics

Standard deviation175.76058
Coefficient of variation (CV)0.35656952
Kurtosis-0.36261066
Mean492.92093
Median Absolute Deviation (MAD)110
Skewness-0.77386737
Sum4868580
Variance30891.781
MonotonicityNot monotonic
2024-05-04T04:23:20.715409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
680 941
 
9.4%
650 919
 
9.2%
620 833
 
8.3%
380 813
 
8.1%
440 762
 
7.6%
410 687
 
6.9%
560 685
 
6.9%
710 618
 
6.2%
530 594
 
5.9%
590 491
 
4.9%
Other values (15) 2534
25.3%
ValueCountFrequency (%)
110 474
4.7%
140 477
4.8%
170 126
 
1.3%
200 73
 
0.7%
210 69
 
0.7%
230 96
 
1.0%
260 85
 
0.9%
290 102
 
1.0%
300 49
 
0.5%
320 23
 
0.2%
ValueCountFrequency (%)
740 95
 
0.9%
710 618
6.2%
680 941
9.4%
650 919
9.2%
620 833
8.3%
590 491
4.9%
560 685
6.9%
540 417
4.2%
530 594
5.9%
500 194
 
1.9%

관할여부
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5942 
<NA>
4040 
2
 
18

Length

Max length4
Median length1
Mean length2.212
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row<NA>
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5942
59.4%
<NA> 4040
40.4%
2 18
 
0.2%

Length

2024-05-04T04:23:21.278135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T04:23:21.675674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5942
59.4%
na 4040
40.4%
2 18
 
0.2%

동코드 (공통)
Real number (ℝ)

ZEROS 

Distinct86
Distinct (%)0.9%
Missing13
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean10801.242
Minimum0
Maximum18700
Zeros320
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:23:22.378900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10100
Q110200
median10700
Q311300
95-th percentile14000
Maximum18700
Range18700
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation2447.3082
Coefficient of variation (CV)0.22657656
Kurtosis11.04103
Mean10801.242
Median Absolute Deviation (MAD)500
Skewness-2.0161697
Sum1.07872 × 108
Variance5989317.5
MonotonicityNot monotonic
2024-05-04T04:23:23.037876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10100 1360
13.6%
10200 1284
 
12.8%
10700 728
 
7.3%
10800 697
 
7.0%
10300 639
 
6.4%
10600 537
 
5.4%
11000 477
 
4.8%
10900 361
 
3.6%
10400 338
 
3.4%
10500 337
 
3.4%
Other values (76) 3229
32.3%
ValueCountFrequency (%)
0 320
 
3.2%
10100 1360
13.6%
10200 1284
12.8%
10300 639
6.4%
10400 338
 
3.4%
10500 337
 
3.4%
10600 537
 
5.4%
10700 728
7.3%
10800 697
7.0%
10900 361
 
3.6%
ValueCountFrequency (%)
18700 3
 
< 0.1%
18600 22
0.2%
18500 10
 
0.1%
18400 27
0.3%
18300 44
0.4%
18200 8
 
0.1%
18100 4
 
< 0.1%
18000 6
 
0.1%
17900 3
 
< 0.1%
17800 4
 
< 0.1%

지번
Text

MISSING 

Distinct7012
Distinct (%)73.7%
Missing481
Missing (%)4.8%
Memory size156.2 KiB
2024-05-04T04:23:24.123265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.065343
Min length2

Characters and Unicode

Total characters57736
Distinct characters41
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5597 ?
Unique (%)58.8%

Sample

1st row140-1도
2nd row380-26대
3rd row42-3도
4th row210-2도
5th row132-54대
ValueCountFrequency (%)
도 872
 
7.5%
대 762
 
6.6%
공 96
 
0.8%
학 86
 
0.7%
임 71
 
0.6%
천 54
 
0.5%
산 20
 
0.2%
산2-1임 19
 
0.2%
잡 19
 
0.2%
전 19
 
0.2%
Other values (6981) 9603
82.6%
2024-05-04T04:23:25.659071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 8081
14.0%
- 7562
13.1%
2 4922
 
8.5%
도 4317
 
7.5%
3 4068
 
7.0%
4 3777
 
6.5%
대 3203
 
5.5%
6 3095
 
5.4%
5 3040
 
5.3%
0 2872
 
5.0%
Other values (31) 12799
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38060
65.9%
Other Letter 10007
 
17.3%
Dash Punctuation 7562
 
13.1%
Space Separator 2102
 
3.6%
Other Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 4317
43.1%
대 3203
32.0%
천 518
 
5.2%
산 499
 
5.0%
임 453
 
4.5%
학 259
 
2.6%
공 167
 
1.7%
제 93
 
0.9%
잡 83
 
0.8%
구 74
 
0.7%
Other values (18) 341
 
3.4%
Decimal Number
ValueCountFrequency (%)
1 8081
21.2%
2 4922
12.9%
3 4068
10.7%
4 3777
9.9%
6 3095
 
8.1%
5 3040
 
8.0%
0 2872
 
7.5%
7 2837
 
7.5%
8 2829
 
7.4%
9 2539
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 7562
100.0%
Space Separator
ValueCountFrequency (%)
2102
100.0%
Other Punctuation
ValueCountFrequency (%)
? 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47729
82.7%
Hangul 10002
 
17.3%
Han 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 4317
43.2%
대 3203
32.0%
천 518
 
5.2%
산 499
 
5.0%
임 453
 
4.5%
학 259
 
2.6%
공 167
 
1.7%
제 93
 
0.9%
잡 83
 
0.8%
구 74
 
0.7%
Other values (15) 336
 
3.4%
Common
ValueCountFrequency (%)
1 8081
16.9%
- 7562
15.8%
2 4922
10.3%
3 4068
8.5%
4 3777
7.9%
6 3095
 
6.5%
5 3040
 
6.4%
0 2872
 
6.0%
7 2837
 
5.9%
8 2829
 
5.9%
Other values (3) 4646
9.7%
Han
ValueCountFrequency (%)
泥 3
60.0%
怨 1
 
20.0%
媛 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47729
82.7%
Hangul 10002
 
17.3%
CJK Compat Ideographs 3
 
< 0.1%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8081
16.9%
- 7562
15.8%
2 4922
10.3%
3 4068
8.5%
4 3777
7.9%
6 3095
 
6.5%
5 3040
 
6.4%
0 2872
 
6.0%
7 2837
 
5.9%
8 2829
 
5.9%
Other values (3) 4646
9.7%
Hangul
ValueCountFrequency (%)
도 4317
43.2%
대 3203
32.0%
천 518
 
5.2%
산 499
 
5.0%
임 453
 
4.5%
학 259
 
2.6%
공 167
 
1.7%
제 93
 
0.9%
잡 83
 
0.8%
구 74
 
0.7%
Other values (15) 336
 
3.4%
CJK Compat Ideographs
ValueCountFrequency (%)
泥 3
100.0%
CJK
ValueCountFrequency (%)
怨 1
50.0%
媛 1
50.0%

비고
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

신경찰서코드 (공통)
Real number (ℝ)

MISSING 

Distinct31
Distinct (%)0.3%
Missing106
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean262.07904
Minimum110
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:23:26.203463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile120
Q1180
median260
Q3340
95-th percentile410
Maximum410
Range300
Interquartile range (IQR)160

Descriptive statistics

Standard deviation89.893708
Coefficient of variation (CV)0.34300228
Kurtosis-1.1974055
Mean262.07904
Median Absolute Deviation (MAD)80
Skewness-0.056699971
Sum2593010
Variance8080.8788
MonotonicityNot monotonic
2024-05-04T04:23:26.600976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
290 777
 
7.8%
210 766
 
7.7%
140 688
 
6.9%
170 687
 
6.9%
340 667
 
6.7%
360 620
 
6.2%
330 594
 
5.9%
410 549
 
5.5%
220 491
 
4.9%
260 475
 
4.8%
Other values (21) 3580
35.8%
ValueCountFrequency (%)
110 257
 
2.6%
120 428
4.3%
130 220
 
2.2%
140 688
6.9%
150 46
 
0.5%
160 126
 
1.3%
170 687
6.9%
180 73
 
0.7%
190 85
 
0.9%
200 96
 
1.0%
ValueCountFrequency (%)
410 549
5.5%
400 23
 
0.2%
390 401
4.0%
380 252
 
2.5%
370 127
 
1.3%
360 620
6.2%
350 129
 
1.3%
340 667
6.7%
330 594
5.9%
320 17
 
0.2%

작업구분 (공통)
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
7173 
4
2318 
2
 
505
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
1 7173
71.7%
4 2318
 
23.2%
2 505
 
5.1%
3 4
 
< 0.1%

Length

2024-05-04T04:23:27.130813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T04:23:27.563537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7173
71.7%
4 2318
 
23.2%
2 505
 
5.1%
3 4
 
< 0.1%

표출구분 (공통)
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5707 
2
4293 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 5707
57.1%
2 4293
42.9%

Length

2024-05-04T04:23:27.917469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T04:23:28.314275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5707
57.1%
2 4293
42.9%

설치방식
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9842 
<NA>
 
98
2
 
60

Length

Max length4
Median length1
Mean length1.0294
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9842
98.4%
<NA> 98
 
1.0%
2 60
 
0.6%

Length

2024-05-04T04:23:28.808162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T04:23:29.294334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9842
98.4%
na 98
 
1.0%
2 60
 
0.6%

도로구분 (공통)
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5039 
2
4837 
<NA>
 
124

Length

Max length4
Median length1
Mean length1.0372
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5039
50.4%
2 4837
48.4%
<NA> 124
 
1.2%

Length

2024-05-04T04:23:29.901481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T04:23:30.318665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5039
50.4%
2 4837
48.4%
na 124
 
1.2%

관할사업소 (공통)
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing17
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean106.21186
Minimum104
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:23:30.606557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile104
Q1105
median106
Q3108
95-th percentile108
Maximum109
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5261337
Coefficient of variation (CV)0.014368769
Kurtosis-1.3535373
Mean106.21186
Median Absolute Deviation (MAD)1
Skewness0.14020222
Sum1060313
Variance2.3290839
MonotonicityNot monotonic
2024-05-04T04:23:30.930854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
108 2897
29.0%
105 2833
28.3%
106 1783
17.8%
104 1338
13.4%
107 783
 
7.8%
109 349
 
3.5%
(Missing) 17
 
0.2%
ValueCountFrequency (%)
104 1338
13.4%
105 2833
28.3%
106 1783
17.8%
107 783
 
7.8%
108 2897
29.0%
109 349
 
3.5%
ValueCountFrequency (%)
109 349
 
3.5%
108 2897
29.0%
107 783
 
7.8%
106 1783
17.8%
105 2833
28.3%
104 1338
13.4%

공간데이터
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

신규정규화ID
Real number (ℝ)

MISSING 

Distinct2415
Distinct (%)99.4%
Missing7571
Missing (%)75.7%
Infinite0
Infinite (%)0.0%
Mean4635085.1
Minimum1
Maximum62403110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:23:31.378229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1155336
Q12246843
median3238303
Q34251443
95-th percentile12075030
Maximum62403110
Range62403109
Interquartile range (IQR)2004600

Descriptive statistics

Standard deviation6741056.3
Coefficient of variation (CV)1.4543544
Kurtosis25.849154
Mean4635085.1
Median Absolute Deviation (MAD)999831
Skewness4.8316625
Sum1.1258622 × 1010
Variance4.5441839 × 1013
MonotonicityNot monotonic
2024-05-04T04:23:31.938685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3
 
< 0.1%
23912710 2
 
< 0.1%
32507010 2
 
< 0.1%
22688110 2
 
< 0.1%
33388510 2
 
< 0.1%
23627710 2
 
< 0.1%
22984310 2
 
< 0.1%
32153010 2
 
< 0.1%
33804010 2
 
< 0.1%
21073010 2
 
< 0.1%
Other values (2405) 2408
 
24.1%
(Missing) 7571
75.7%
ValueCountFrequency (%)
1 3
< 0.1%
184442 1
 
< 0.1%
191355 1
 
< 0.1%
192462 1
 
< 0.1%
192588 1
 
< 0.1%
193607 1
 
< 0.1%
193698 1
 
< 0.1%
193943 1
 
< 0.1%
194282 1
 
< 0.1%
194482 1
 
< 0.1%
ValueCountFrequency (%)
62403110 1
< 0.1%
61058010 2
< 0.1%
54966110 1
< 0.1%
54925010 1
< 0.1%
53622210 1
< 0.1%
53156410 1
< 0.1%
53124810 1
< 0.1%
51888610 1
< 0.1%
51530310 1
< 0.1%
51530110 1
< 0.1%

참조코드
Real number (ℝ)

MISSING 

Distinct1703
Distinct (%)47.8%
Missing6434
Missing (%)64.3%
Infinite0
Infinite (%)0.0%
Mean1.1609887 × 109
Minimum1.0000001 × 109
Maximum2.2700085 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:23:32.549747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0000001 × 109
5-th percentile1.0100034 × 109
Q11.1200026 × 109
median1.1800088 × 109
Q31.2100122 × 109
95-th percentile1.2300204 × 109
Maximum2.2700085 × 109
Range1.2700084 × 109
Interquartile range (IQR)90009575

Descriptive statistics

Standard deviation1.0284388 × 108
Coefficient of variation (CV)0.08858302
Kurtosis49.688894
Mean1.1609887 × 109
Median Absolute Deviation (MAD)40009350
Skewness5.061109
Sum4.1400857 × 1012
Variance1.0576865 × 1016
MonotonicityNot monotonic
2024-05-04T04:23:33.449497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1210006600 18
 
0.2%
1170002600 14
 
0.1%
1120001300 12
 
0.1%
1200000800 11
 
0.1%
1200012200 11
 
0.1%
1200006500 11
 
0.1%
1000021200 10
 
0.1%
1220026300 10
 
0.1%
1170002500 10
 
0.1%
1200000900 10
 
0.1%
Other values (1693) 3449
34.5%
(Missing) 6434
64.3%
ValueCountFrequency (%)
1000000100 2
< 0.1%
1000000500 1
 
< 0.1%
1000000600 1
 
< 0.1%
1000001000 3
< 0.1%
1000001300 3
< 0.1%
1000003500 2
< 0.1%
1000004100 1
 
< 0.1%
1000004500 2
< 0.1%
1000004600 1
 
< 0.1%
1000004700 1
 
< 0.1%
ValueCountFrequency (%)
2270008500 1
 
< 0.1%
2210040200 1
 
< 0.1%
2210040100 2
< 0.1%
2130043200 1
 
< 0.1%
2130027300 1
 
< 0.1%
2130020800 1
 
< 0.1%
2130018700 1
 
< 0.1%
2130018600 3
< 0.1%
2130006300 1
 
< 0.1%
2130005700 1
 
< 0.1%

이력ID
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35899.53
Minimum4
Maximum275683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:23:34.099837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile2559.9
Q112641
median24903.5
Q338189.25
95-th percentile210347.75
Maximum275683
Range275679
Interquartile range (IQR)25548.25

Descriptive statistics

Standard deviation49588.113
Coefficient of variation (CV)1.3813026
Kurtosis10.102173
Mean35899.53
Median Absolute Deviation (MAD)12775
Skewness3.2835233
Sum3.589953 × 108
Variance2.458981 × 109
MonotonicityNot monotonic
2024-05-04T04:23:34.522208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17567 1
 
< 0.1%
10422 1
 
< 0.1%
1142 1
 
< 0.1%
14054 1
 
< 0.1%
24650 1
 
< 0.1%
40445 1
 
< 0.1%
8560 1
 
< 0.1%
10862 1
 
< 0.1%
6700 1
 
< 0.1%
10827 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
4 1
< 0.1%
7 1
< 0.1%
17 1
< 0.1%
20 1
< 0.1%
37 1
< 0.1%
39 1
< 0.1%
45 1
< 0.1%
48 1
< 0.1%
53 1
< 0.1%
63 1
< 0.1%
ValueCountFrequency (%)
275683 1
< 0.1%
275600 1
< 0.1%
275531 1
< 0.1%
275238 1
< 0.1%
274942 1
< 0.1%
274865 1
< 0.1%
274850 1
< 0.1%
274822 1
< 0.1%
274766 1
< 0.1%
274761 1
< 0.1%

공사관리번호
Text

MISSING 

Distinct872
Distinct (%)9.0%
Missing285
Missing (%)2.9%
Memory size156.2 KiB
2024-05-04T04:23:35.387020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters136010
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique420 ?
Unique (%)4.3%

Sample

1st row2006-0201-029
2nd row2000-0000-000
3rd row2005-0101-053
4th row2004-0101-050
5th row2014-0101-072
ValueCountFrequency (%)
2000-0000-000 7292
75.1%
2007-0201-457 34
 
0.3%
2008-0201-004 29
 
0.3%
2008-0201-006 29
 
0.3%
2008-1201-719 28
 
0.3%
2007-0201-473 25
 
0.3%
2007-0201-663 23
 
0.2%
2004-0101-042 18
 
0.2%
2004-0201-004 15
 
0.2%
2010-1101-153 15
 
0.2%
Other values (862) 2207
 
22.7%
2024-05-04T04:23:36.690262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 84016
61.8%
- 19430
 
14.3%
2 10963
 
8.1%
9715
 
7.1%
1 5961
 
4.4%
4 1228
 
0.9%
8 897
 
0.7%
5 868
 
0.6%
3 823
 
0.6%
7 782
 
0.6%
Other values (2) 1327
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 106865
78.6%
Dash Punctuation 19430
 
14.3%
Space Separator 9715
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 84016
78.6%
2 10963
 
10.3%
1 5961
 
5.6%
4 1228
 
1.1%
8 897
 
0.8%
5 868
 
0.8%
3 823
 
0.8%
7 782
 
0.7%
6 690
 
0.6%
9 637
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 19430
100.0%
Space Separator
ValueCountFrequency (%)
9715
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 136010
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 84016
61.8%
- 19430
 
14.3%
2 10963
 
8.1%
9715
 
7.1%
1 5961
 
4.4%
4 1228
 
0.9%
8 897
 
0.7%
5 868
 
0.6%
3 823
 
0.6%
7 782
 
0.6%
Other values (2) 1327
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136010
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 84016
61.8%
- 19430
 
14.3%
2 10963
 
8.1%
9715
 
7.1%
1 5961
 
4.4%
4 1228
 
0.9%
8 897
 
0.7%
5 868
 
0.6%
3 823
 
0.6%
7 782
 
0.6%
Other values (2) 1327
 
1.0%

지주관리번호.1
Text

Distinct9896
Distinct (%)99.2%
Missing24
Missing (%)0.2%
Memory size156.2 KiB
2024-05-04T04:23:37.731890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters89784
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9816 ?
Unique (%)98.4%

Sample

1st row02-012385
2nd row02-014383
3rd row02-010174
4th row02-014409
5th row02-180282
ValueCountFrequency (%)
02-150745 2
 
< 0.1%
02-017860 2
 
< 0.1%
02-017663 2
 
< 0.1%
02-026562 2
 
< 0.1%
02-016057 2
 
< 0.1%
02-079349 2
 
< 0.1%
02-023593 2
 
< 0.1%
02-003145 2
 
< 0.1%
02-006415 2
 
< 0.1%
02-022711 2
 
< 0.1%
Other values (9886) 9956
99.8%
2024-05-04T04:23:39.245109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 24400
27.2%
2 15543
17.3%
- 9976
11.1%
1 7959
 
8.9%
3 5437
 
6.1%
9 4654
 
5.2%
4 4573
 
5.1%
5 4530
 
5.0%
7 4448
 
5.0%
6 4156
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79808
88.9%
Dash Punctuation 9976
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24400
30.6%
2 15543
19.5%
1 7959
 
10.0%
3 5437
 
6.8%
9 4654
 
5.8%
4 4573
 
5.7%
5 4530
 
5.7%
7 4448
 
5.6%
6 4156
 
5.2%
8 4108
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 9976
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 89784
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24400
27.2%
2 15543
17.3%
- 9976
11.1%
1 7959
 
8.9%
3 5437
 
6.1%
9 4654
 
5.2%
4 4573
 
5.1%
5 4530
 
5.0%
7 4448
 
5.0%
6 4156
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24400
27.2%
2 15543
17.3%
- 9976
11.1%
1 7959
 
8.9%
3 5437
 
6.1%
9 4654
 
5.2%
4 4573
 
5.1%
5 4530
 
5.0%
7 4448
 
5.0%
6 4156
 
4.6%

공사형태 (공통)
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)0.1%
Missing2590
Missing (%)25.9%
Infinite0
Infinite (%)0.0%
Mean1.6326586
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:23:39.606691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0881078
Coefficient of variation (CV)0.66646375
Kurtosis2.5200131
Mean1.6326586
Median Absolute Deviation (MAD)0
Skewness1.6949984
Sum12098
Variance1.1839785
MonotonicityNot monotonic
2024-05-04T04:23:40.135843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 5229
52.3%
3 1506
 
15.1%
2 320
 
3.2%
5 143
 
1.4%
4 140
 
1.4%
6 69
 
0.7%
7 2
 
< 0.1%
8 1
 
< 0.1%
(Missing) 2590
25.9%
ValueCountFrequency (%)
1 5229
52.3%
2 320
 
3.2%
3 1506
 
15.1%
4 140
 
1.4%
5 143
 
1.4%
6 69
 
0.7%
7 2
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 2
 
< 0.1%
6 69
 
0.7%
5 143
 
1.4%
4 140
 
1.4%
3 1506
 
15.1%
2 320
 
3.2%
1 5229
52.3%

Sample

상태 (공통)지주관리번호형식코드규격코드고가 (공통)용융도금교차로코드설치일교체일구경찰서코드 (공통)구코드 (공통)관할여부동코드 (공통)지번비고신경찰서코드 (공통)작업구분 (공통)표출구분 (공통)설치방식도로구분 (공통)관할사업소 (공통)공간데이터신규정규화ID참조코드이력ID공사관리번호지주관리번호.1공사형태 (공통)
18381102-000001238516117301010120010101120110110500140-1도<NA>1204111107<NA><NA><NA>175672006-0201-02902-012385<NA>
13120102-00000143838101102000010120000101210440<NA>12200380-26대<NA>2101112108<NA><NA><NA>117122000-0000-00002-0143833
7306102-000001017416111073101012001010113014011660042-3도<NA>1304111108<NA><NA><NA>50322005-0101-05302-010174<NA>
17141102-0000014409121155541010119870101140410111700210-2도<NA>1404111108<NA><NA>1120004800133812004-0101-05002-014409<NA>
320102-000018028216122802014110820141108160170111900132-54대<NA>1601211108<NA>3234839<NA>2316982014-0101-07202-1802824
11666102-0000105193741202016101520161015140410<NA>12000222-2도<NA>1401211108<NA>2361489<NA>93662000-0000-00002-1051931
14709102-0000015549141155301010120010101300500110400814 도<NA>3004111104<NA><NA><NA>121292000-0000-00002-015549<NA>
26928102-00001067137101102000010120000101260620110200산197-58도<NA>2901111105<NA><NA>1200002200254332000-0000-00002-106713<NA>
18613102-00001543437101102000010120000101140410111700120-211대<NA>1401212108<NA><NA>1200004000163642000-0000-00002-1543431
8660102-00000005258101102000010120000101110140<NA>15500137-1 도<NA>1101112108<NA><NA><NA>53022000-0000-00002-0005253
상태 (공통)지주관리번호형식코드규격코드고가 (공통)용융도금교차로코드설치일교체일구경찰서코드 (공통)구코드 (공통)관할여부동코드 (공통)지번비고신경찰서코드 (공통)작업구분 (공통)표출구분 (공통)설치방식도로구분 (공통)관할사업소 (공통)공간데이터신규정규화ID참조코드이력ID공사관리번호지주관리번호.1공사형태 (공통)
35992102-00001081938101102000010120000101170560<NA>132002188 대<NA>1701212105<NA><NA><NA>351822000-0000-00002-1081931
11260102-0000004800611102000010120000101300500<NA>109002-2천<NA>3001212104<NA><NA><NA>79622000-0000-00002-0048001
34345102-000010843571011020000101200001012205901103002-10대<NA>2201211105<NA><NA>1190004800333972000-0000-00002-1084351
4743102-0000008722810110200001012000010114041011190075-40대<NA>1401112108<NA>2352413<NA>23232000-0000-00002-0087221
28284102-00000224658101102000010120000101290620<NA>10200675-38대<NA>2901212105<NA><NA><NA>271112000-0000-00002-0224651
8641102-0000010252111102000010120000101110140<NA>13400130-1대<NA>1101111108<NA><NA><NA>72722000-0000-00002-0102523
26428102-00001078088101101010110101330530110700132-29도<NA>3304112104<NA><NA><NA>246692000-0000-00002-107808<NA>
2462102-0000009025161131082007013120070131240410112000185-2도<NA>1404112108<NA><NA>112000770010762006-1301-01502-0090254
21057102-000005246216116751010120010901120110114100175-92도<NA>1204111107<NA><NA><NA>201902009-0401-00302-0524622
3276102-0000150860811102018120320181203230210110100273-2 도<NA>2301122109<NA>5259334<NA>13012008-0107-56602-1508603