Overview

Dataset statistics

Number of variables25
Number of observations10000
Missing cells55070
Missing cells (%)22.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory228.0 B

Variable types

Text5
Categorical7
Numeric12
Unsupported1

Dataset

Description부착대관리번호,상태 (공통),부착방식,부착대길이,고가 (공통),부착대방향,신호등수량,배면등수량,신호등종류,배면등종류,설치일,교체일,지주관리번호,신호발광구분,제조회사,작업구분 (공통),표출구분 (공통),신규정규화ID,공사관리번호,부착대관리번호,이력ID,위치정보,X좌표,Y좌표,공사형태 (공통)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15546/S/1/datasetView.do

Alerts

상태 (공통) is highly imbalanced (99.6%)Imbalance
부착방식 is highly imbalanced (52.9%)Imbalance
고가 (공통) is highly imbalanced (90.4%)Imbalance
공사형태 (공통) is highly imbalanced (89.0%)Imbalance
부착대길이 has 339 (3.4%) missing valuesMissing
배면등종류 has 6794 (67.9%) missing valuesMissing
설치일 has 9822 (98.2%) missing valuesMissing
교체일 has 9812 (98.1%) missing valuesMissing
제조회사 has 8459 (84.6%) missing valuesMissing
신규정규화ID has 9783 (97.8%) missing valuesMissing
위치정보 has 10000 (100.0%) missing valuesMissing
이력ID has unique valuesUnique
위치정보 is an unsupported type, check if it needs cleaning or further analysisUnsupported
부착대방향 has 317 (3.2%) zerosZeros
배면등수량 has 8703 (87.0%) zerosZeros

Reproduction

Analysis started2024-05-03 21:54:22.185760
Analysis finished2024-05-03 21:54:24.375637
Duration2.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

부착대관리번호
Text

Distinct9253
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T21:54:24.789226image/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

Unique8548 ?
Unique (%)85.5%

Sample

1st row03-0000014821
2nd row03-0000050443
3rd row03-0000009436
4th row03-0000044463
5th row03-0000026881
ValueCountFrequency (%)
03-0000018942 4
 
< 0.1%
03-0000014203 4
 
< 0.1%
03-0000005623 4
 
< 0.1%
03-0000057579 4
 
< 0.1%
03-0000005064 4
 
< 0.1%
03-0000049511 3
 
< 0.1%
03-0000044086 3
 
< 0.1%
03-0000017786 3
 
< 0.1%
03-0000000290 3
 
< 0.1%
03-0000007053 3
 
< 0.1%
Other values (9243) 9965
99.7%
2024-05-03T21:54:26.270257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 66141
50.9%
3 15188
 
11.7%
- 10000
 
7.7%
1 5946
 
4.6%
5 5746
 
4.4%
2 5526
 
4.3%
4 5343
 
4.1%
6 4346
 
3.3%
7 4124
 
3.2%
8 3929
 
3.0%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66141
55.1%
3 15188
 
12.7%
1 5946
 
5.0%
5 5746
 
4.8%
2 5526
 
4.6%
4 5343
 
4.5%
6 4346
 
3.6%
7 4124
 
3.4%
8 3929
 
3.3%
9 3711
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66141
50.9%
3 15188
 
11.7%
- 10000
 
7.7%
1 5946
 
4.6%
5 5746
 
4.4%
2 5526
 
4.3%
4 5343
 
4.1%
6 4346
 
3.3%
7 4124
 
3.2%
8 3929
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66141
50.9%
3 15188
 
11.7%
- 10000
 
7.7%
1 5946
 
4.6%
5 5746
 
4.4%
2 5526
 
4.3%
4 5343
 
4.1%
6 4346
 
3.3%
7 4124
 
3.2%
8 3929
 
3.0%

상태 (공통)
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9997 
<NA>
 
3

Length

Max length4
Median length1
Mean length1.0009
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 9997
> 99.9%
<NA> 3
 
< 0.1%

Length

2024-05-03T21:54:26.734954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T21:54:27.213674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9997
> 99.9%
na 3
 
< 0.1%

부착방식
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3
5102 
2
4474 
4
 
412
5
 
8
1
 
3

Length

Max length4
Median length1
Mean length1.0003
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row3
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3 5102
51.0%
2 4474
44.7%
4 412
 
4.1%
5 8
 
0.1%
1 3
 
< 0.1%
<NA> 1
 
< 0.1%

Length

2024-05-03T21:54:27.785060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T21:54:28.128458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 5102
51.0%
2 4474
44.7%
4 412
 
4.1%
5 8
 
0.1%
1 3
 
< 0.1%
na 1
 
< 0.1%

부착대길이
Real number (ℝ)

MISSING 

Distinct16
Distinct (%)0.2%
Missing339
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean482.15081
Minimum0
Maximum999
Zeros95
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T21:54:28.570996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median11
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)992

Descriptive statistics

Standard deviation495.75126
Coefficient of variation (CV)1.0282079
Kurtosis-1.9933695
Mean482.15081
Median Absolute Deviation (MAD)10
Skewness0.083455176
Sum4658059
Variance245769.32
MonotonicityNot monotonic
2024-05-03T21:54:28.963870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
999 4629
46.3%
7 1338
 
13.4%
9 794
 
7.9%
11 770
 
7.7%
5 672
 
6.7%
6 549
 
5.5%
1 501
 
5.0%
3 234
 
2.3%
0 95
 
0.9%
12 54
 
0.5%
Other values (6) 25
 
0.2%
(Missing) 339
 
3.4%
ValueCountFrequency (%)
0 95
 
0.9%
1 501
 
5.0%
2 3
 
< 0.1%
3 234
 
2.3%
4 3
 
< 0.1%
5 672
6.7%
6 549
5.5%
7 1338
13.4%
8 9
 
0.1%
9 794
7.9%
ValueCountFrequency (%)
999 4629
46.3%
15 1
 
< 0.1%
13 2
 
< 0.1%
12 54
 
0.5%
11 770
 
7.7%
10 7
 
0.1%
9 794
 
7.9%
8 9
 
0.1%
7 1338
 
13.4%
6 549
 
5.5%

고가 (공통)
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9758 
<NA>
 
150
2
 
81
0
 
11

Length

Max length4
Median length1
Mean length1.045
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 9758
97.6%
<NA> 150
 
1.5%
2 81
 
0.8%
0 11
 
0.1%

Length

2024-05-03T21:54:29.587790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T21:54:29.941444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9758
97.6%
na 150
 
1.5%
2 81
 
0.8%
0 11
 
0.1%

부착대방향
Real number (ℝ)

ZEROS 

Distinct361
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.1409
Minimum0
Maximum360
Zeros317
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T21:54:30.292535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q186
median178
Q3270
95-th percentile341
Maximum360
Range360
Interquartile range (IQR)184

Descriptive statistics

Standard deviation106.23995
Coefficient of variation (CV)0.61008042
Kurtosis-1.1854528
Mean174.1409
Median Absolute Deviation (MAD)92
Skewness-0.0015014874
Sum1741409
Variance11286.928
MonotonicityNot monotonic
2024-05-03T21:54:30.861211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 317
 
3.2%
180 100
 
1.0%
270 73
 
0.7%
90 64
 
0.6%
201 61
 
0.6%
291 56
 
0.6%
202 51
 
0.5%
271 50
 
0.5%
199 50
 
0.5%
19 48
 
0.5%
Other values (351) 9130
91.3%
ValueCountFrequency (%)
0 317
3.2%
1 26
 
0.3%
2 30
 
0.3%
3 28
 
0.3%
4 29
 
0.3%
5 21
 
0.2%
6 26
 
0.3%
7 28
 
0.3%
8 20
 
0.2%
9 22
 
0.2%
ValueCountFrequency (%)
360 36
0.4%
359 34
0.3%
358 25
0.2%
357 31
0.3%
356 36
0.4%
355 27
0.3%
354 22
0.2%
353 19
0.2%
352 17
0.2%
351 17
0.2%

신호등수량
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5065
Minimum0
Maximum8
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T21:54:31.450777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7543306
Coefficient of variation (CV)0.50071729
Kurtosis2.30454
Mean1.5065
Median Absolute Deviation (MAD)0
Skewness1.5125507
Sum15065
Variance0.56901465
MonotonicityNot monotonic
2024-05-03T21:54:31.964030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 6255
62.5%
2 2649
26.5%
3 873
 
8.7%
4 209
 
2.1%
5 5
 
0.1%
0 4
 
< 0.1%
6 4
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 6255
62.5%
2 2649
26.5%
3 873
 
8.7%
4 209
 
2.1%
5 5
 
0.1%
6 4
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 4
 
< 0.1%
5 5
 
0.1%
4 209
 
2.1%
3 873
 
8.7%
2 2649
26.5%
1 6255
62.5%
0 4
 
< 0.1%

배면등수량
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1784
Minimum0
Maximum5
Zeros8703
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T21:54:32.308679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.50477611
Coefficient of variation (CV)2.8294625
Kurtosis10.93546
Mean0.1784
Median Absolute Deviation (MAD)0
Skewness3.1387014
Sum1784
Variance0.25479892
MonotonicityNot monotonic
2024-05-03T21:54:32.811855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 8703
87.0%
1 851
 
8.5%
2 414
 
4.1%
3 27
 
0.3%
5 4
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
0 8703
87.0%
1 851
 
8.5%
2 414
 
4.1%
3 27
 
0.3%
4 1
 
< 0.1%
5 4
 
< 0.1%
ValueCountFrequency (%)
5 4
 
< 0.1%
4 1
 
< 0.1%
3 27
 
0.3%
2 414
 
4.1%
1 851
 
8.5%
0 8703
87.0%

신호등종류
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.0108011
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T21:54:33.372794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q37
95-th percentile10
Maximum21
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2084174
Coefficient of variation (CV)0.64030029
Kurtosis0.14695305
Mean5.0108011
Median Absolute Deviation (MAD)4
Skewness0.63078921
Sum50103
Variance10.293942
MonotonicityNot monotonic
2024-05-03T21:54:34.031366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
7 4148
41.5%
2 2594
25.9%
3 1249
 
12.5%
1 996
 
10.0%
10 500
 
5.0%
12 287
 
2.9%
14 128
 
1.3%
5 24
 
0.2%
4 19
 
0.2%
19 11
 
0.1%
Other values (8) 43
 
0.4%
ValueCountFrequency (%)
1 996
 
10.0%
2 2594
25.9%
3 1249
 
12.5%
4 19
 
0.2%
5 24
 
0.2%
6 8
 
0.1%
7 4148
41.5%
8 10
 
0.1%
9 7
 
0.1%
10 500
 
5.0%
ValueCountFrequency (%)
21 4
 
< 0.1%
20 3
 
< 0.1%
19 11
 
0.1%
17 2
 
< 0.1%
16 3
 
< 0.1%
14 128
 
1.3%
13 6
 
0.1%
12 287
2.9%
10 500
5.0%
9 7
 
0.1%

배면등종류
Real number (ℝ)

MISSING 

Distinct11
Distinct (%)0.3%
Missing6794
Missing (%)67.9%
Infinite0
Infinite (%)0.0%
Mean2.6671865
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T21:54:34.460983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile6.75
Maximum21
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.0852036
Coefficient of variation (CV)1.5316528
Kurtosis15.24546
Mean2.6671865
Median Absolute Deviation (MAD)1
Skewness4.0506073
Sum8551
Variance16.688889
MonotonicityNot monotonic
2024-05-03T21:54:34.966141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 1330
 
13.3%
2 1155
 
11.6%
3 524
 
5.2%
21 142
 
1.4%
4 18
 
0.2%
5 15
 
0.1%
8 11
 
0.1%
16 4
 
< 0.1%
6 3
 
< 0.1%
7 3
 
< 0.1%
(Missing) 6794
67.9%
ValueCountFrequency (%)
1 1330
13.3%
2 1155
11.6%
3 524
 
5.2%
4 18
 
0.2%
5 15
 
0.1%
6 3
 
< 0.1%
7 3
 
< 0.1%
8 11
 
0.1%
16 4
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
21 142
 
1.4%
19 1
 
< 0.1%
16 4
 
< 0.1%
8 11
 
0.1%
7 3
 
< 0.1%
6 3
 
< 0.1%
5 15
 
0.1%
4 18
 
0.2%
3 524
5.2%
2 1155
11.6%

설치일
Real number (ℝ)

MISSING 

Distinct123
Distinct (%)69.1%
Missing9822
Missing (%)98.2%
Infinite0
Infinite (%)0.0%
Mean20143927
Minimum20000101
Maximum20180223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T21:54:35.503717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20000101
5-th percentile20130618
Q120140111
median20141024
Q320150310
95-th percentile20170909
Maximum20180223
Range180122
Interquartile range (IQR)10199

Descriptive statistics

Standard deviation18012.978
Coefficient of variation (CV)0.00089421381
Kurtosis25.807828
Mean20143927
Median Absolute Deviation (MAD)9285
Skewness-3.5295635
Sum3.585619 × 109
Variance3.2446737 × 108
MonotonicityNot monotonic
2024-05-03T21:54:36.000803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20140730 6
 
0.1%
20130621 4
 
< 0.1%
20141120 4
 
< 0.1%
20150310 4
 
< 0.1%
20131220 4
 
< 0.1%
20150304 3
 
< 0.1%
20150309 3
 
< 0.1%
20140826 3
 
< 0.1%
20140109 3
 
< 0.1%
20150305 3
 
< 0.1%
Other values (113) 141
 
1.4%
(Missing) 9822
98.2%
ValueCountFrequency (%)
20000101 1
< 0.1%
20050530 1
< 0.1%
20100101 1
< 0.1%
20110929 1
< 0.1%
20121208 1
< 0.1%
20130530 1
< 0.1%
20130531 1
< 0.1%
20130601 1
< 0.1%
20130615 1
< 0.1%
20130619 1
< 0.1%
ValueCountFrequency (%)
20180223 1
< 0.1%
20171226 2
< 0.1%
20171218 2
< 0.1%
20171217 1
< 0.1%
20171215 1
< 0.1%
20171126 1
< 0.1%
20170920 1
< 0.1%
20170907 1
< 0.1%
20170430 2
< 0.1%
20161231 1
< 0.1%

교체일
Real number (ℝ)

MISSING 

Distinct129
Distinct (%)68.6%
Missing9812
Missing (%)98.1%
Infinite0
Infinite (%)0.0%
Mean20144094
Minimum20000101
Maximum20180223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T21:54:36.476482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20000101
5-th percentile20130621
Q120131231
median20140972
Q320150310
95-th percentile20170915
Maximum20180223
Range180122
Interquartile range (IQR)19079.5

Descriptive statistics

Standard deviation17671.465
Coefficient of variation (CV)0.00087725288
Kurtosis26.368243
Mean20144094
Median Absolute Deviation (MAD)9347
Skewness-3.4649539
Sum3.7870897 × 109
Variance3.1228066 × 108
MonotonicityNot monotonic
2024-05-03T21:54:36.958021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20140730 6
 
0.1%
20131220 4
 
< 0.1%
20150310 4
 
< 0.1%
20130621 4
 
< 0.1%
20160608 3
 
< 0.1%
20150305 3
 
< 0.1%
20150309 3
 
< 0.1%
20140109 3
 
< 0.1%
20140826 3
 
< 0.1%
20150306 3
 
< 0.1%
Other values (119) 152
 
1.5%
(Missing) 9812
98.1%
ValueCountFrequency (%)
20000101 1
 
< 0.1%
20050530 1
 
< 0.1%
20110929 1
 
< 0.1%
20121208 1
 
< 0.1%
20130530 1
 
< 0.1%
20130531 1
 
< 0.1%
20130601 1
 
< 0.1%
20130615 1
 
< 0.1%
20130619 1
 
< 0.1%
20130621 4
< 0.1%
ValueCountFrequency (%)
20180223 1
< 0.1%
20171226 2
< 0.1%
20171218 2
< 0.1%
20171217 1
< 0.1%
20171215 1
< 0.1%
20171126 1
< 0.1%
20171118 1
< 0.1%
20170920 1
< 0.1%
20170907 1
< 0.1%
20170430 2
< 0.1%

지주관리번호
Text

Distinct8356
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T21:54:37.500565image/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

Unique7024 ?
Unique (%)70.2%

Sample

1st row02-0000045349
2nd row02-0000152907
3rd row02-0000095959
4th row02-0000132178
5th row02-0000103565
ValueCountFrequency (%)
02-0000077101 7
 
0.1%
02-0000109795 6
 
0.1%
02-0000167116 6
 
0.1%
02-0000156323 5
 
< 0.1%
02-0000137729 5
 
< 0.1%
02-0000114199 5
 
< 0.1%
02-0000077390 5
 
< 0.1%
02-0000010011 5
 
< 0.1%
02-0000023353 5
 
< 0.1%
02-0000076849 5
 
< 0.1%
Other values (8346) 9946
99.5%
2024-05-03T21:54:38.392553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 61320
47.2%
2 15180
 
11.7%
- 10000
 
7.7%
1 9565
 
7.4%
5 5238
 
4.0%
6 5202
 
4.0%
3 5045
 
3.9%
8 4785
 
3.7%
7 4758
 
3.7%
4 4660
 
3.6%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 61320
51.1%
2 15180
 
12.7%
1 9565
 
8.0%
5 5238
 
4.4%
6 5202
 
4.3%
3 5045
 
4.2%
8 4785
 
4.0%
7 4758
 
4.0%
4 4660
 
3.9%
9 4247
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 61320
47.2%
2 15180
 
11.7%
- 10000
 
7.7%
1 9565
 
7.4%
5 5238
 
4.0%
6 5202
 
4.0%
3 5045
 
3.9%
8 4785
 
3.7%
7 4758
 
3.7%
4 4660
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 61320
47.2%
2 15180
 
11.7%
- 10000
 
7.7%
1 9565
 
7.4%
5 5238
 
4.0%
6 5202
 
4.0%
3 5045
 
3.9%
8 4785
 
3.7%
7 4758
 
3.7%
4 4660
 
3.6%

신호발광구분
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
7122 
1
2878 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 7122
71.2%
1 2878
28.8%

Length

2024-05-03T21:54:38.791028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T21:54:39.090866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 7122
71.2%
1 2878
28.8%

제조회사
Text

MISSING 

Distinct81
Distinct (%)5.3%
Missing8459
Missing (%)84.6%
Memory size156.2 KiB
2024-05-03T21:54:39.470409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length3.578196
Min length1

Characters and Unicode

Total characters5514
Distinct characters113
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)1.9%

Sample

1st row0
2nd row.
3rd row진우산전
4th row구전산업
5th row노인복지회
ValueCountFrequency (%)
0 528
32.5%
진우산전 194
 
11.9%
구전산업 105
 
6.5%
삼일신호공사 89
 
5.5%
한국노인회관 82
 
5.0%
지원제단 82
 
5.0%
51
 
3.1%
구전산업(주 38
 
2.3%
진우산전(주 35
 
2.2%
기타 34
 
2.1%
Other values (69) 386
23.8%
2024-05-03T21:54:40.185698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 528
 
9.6%
전 476
 
8.6%
산 406
 
7.4%
우 276
 
5.0%
진 276
 
5.0%
구 172
 
3.1%
한 167
 
3.0%
업 155
 
2.8%
신 142
 
2.6%
국 130
 
2.4%
Other values (103) 2786
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4534
82.2%
Decimal Number 560
 
10.2%
Open Punctuation 125
 
2.3%
Close Punctuation 125
 
2.3%
Space Separator 83
 
1.5%
Other Punctuation 48
 
0.9%
Uppercase Letter 28
 
0.5%
Lowercase Letter 5
 
0.1%
Other Symbol 3
 
0.1%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 476
 
10.5%
산 406
 
9.0%
우 276
 
6.1%
진 276
 
6.1%
구 172
 
3.8%
한 167
 
3.7%
업 155
 
3.4%
신 142
 
3.1%
국 130
 
2.9%
주 123
 
2.7%
Other values (82) 2211
48.8%
Decimal Number
ValueCountFrequency (%)
0 528
94.3%
1 19
 
3.4%
2 7
 
1.2%
3 3
 
0.5%
5 3
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
T 12
42.9%
N 6
21.4%
I 6
21.4%
S 2
 
7.1%
F 2
 
7.1%
Lowercase Letter
ValueCountFrequency (%)
t 2
40.0%
n 1
20.0%
i 1
20.0%
q 1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 45
93.8%
, 3
 
6.2%
Open Punctuation
ValueCountFrequency (%)
( 125
100.0%
Close Punctuation
ValueCountFrequency (%)
) 125
100.0%
Space Separator
ValueCountFrequency (%)
83
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4537
82.3%
Common 944
 
17.1%
Latin 33
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 476
 
10.5%
산 406
 
8.9%
우 276
 
6.1%
진 276
 
6.1%
구 172
 
3.8%
한 167
 
3.7%
업 155
 
3.4%
신 142
 
3.1%
국 130
 
2.9%
주 123
 
2.7%
Other values (83) 2214
48.8%
Common
ValueCountFrequency (%)
0 528
55.9%
( 125
 
13.2%
) 125
 
13.2%
83
 
8.8%
. 45
 
4.8%
1 19
 
2.0%
2 7
 
0.7%
3 3
 
0.3%
5 3
 
0.3%
, 3
 
0.3%
Latin
ValueCountFrequency (%)
T 12
36.4%
N 6
18.2%
I 6
18.2%
S 2
 
6.1%
t 2
 
6.1%
F 2
 
6.1%
n 1
 
3.0%
i 1
 
3.0%
q 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4532
82.2%
ASCII 977
 
17.7%
None 3
 
0.1%
Compat Jamo 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 528
54.0%
( 125
 
12.8%
) 125
 
12.8%
83
 
8.5%
. 45
 
4.6%
1 19
 
1.9%
T 12
 
1.2%
2 7
 
0.7%
N 6
 
0.6%
I 6
 
0.6%
Other values (10) 21
 
2.1%
Hangul
ValueCountFrequency (%)
전 476
 
10.5%
산 406
 
9.0%
우 276
 
6.1%
진 276
 
6.1%
구 172
 
3.8%
한 167
 
3.7%
업 155
 
3.4%
신 142
 
3.1%
국 130
 
2.9%
주 123
 
2.7%
Other values (80) 2209
48.7%
None
ValueCountFrequency (%)
㈜ 3
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅎ 1
50.0%
ㅛ 1
50.0%

작업구분 (공통)
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
3932 
4
3655 
2
1995 
3
 
222
6
 
196

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row4
4th row4
5th row2

Common Values

ValueCountFrequency (%)
1 3932
39.3%
4 3655
36.5%
2 1995
20.0%
3 222
 
2.2%
6 196
 
2.0%

Length

2024-05-03T21:54:40.752377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T21:54:41.344259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3932
39.3%
4 3655
36.5%
2 1995
20.0%
3 222
 
2.2%
6 196
 
2.0%

표출구분 (공통)
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
6529 
2
3470 
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0003
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1 6529
65.3%
2 3470
34.7%
<NA> 1
 
< 0.1%

Length

2024-05-03T21:54:41.815624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T21:54:42.250998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6529
65.3%
2 3470
34.7%
na 1
 
< 0.1%

신규정규화ID
Real number (ℝ)

MISSING 

Distinct217
Distinct (%)100.0%
Missing9783
Missing (%)97.8%
Infinite0
Infinite (%)0.0%
Mean2956842.2
Minimum1
Maximum6241793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T21:54:42.701871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1118283.4
Q12117595
median2333021
Q34320951
95-th percentile6131391
Maximum6241793
Range6241792
Interquartile range (IQR)2203356

Descriptive statistics

Standard deviation1586005
Coefficient of variation (CV)0.53638473
Kurtosis-0.7373238
Mean2956842.2
Median Absolute Deviation (MAD)1068458
Skewness0.47198602
Sum6.4163475 × 108
Variance2.5154118 × 1012
MonotonicityNot monotonic
2024-05-03T21:54:43.244053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2372722 1
 
< 0.1%
2266611 1
 
< 0.1%
2262181 1
 
< 0.1%
1121685 1
 
< 0.1%
4306301 1
 
< 0.1%
6174991 1
 
< 0.1%
2230852 1
 
< 0.1%
6132791 1
 
< 0.1%
1209023 1
 
< 0.1%
2232911 1
 
< 0.1%
Other values (207) 207
 
2.1%
(Missing) 9783
97.8%
ValueCountFrequency (%)
1 1
< 0.1%
193171 1
< 0.1%
286064 1
< 0.1%
286066 1
< 0.1%
287141 1
< 0.1%
287541 1
< 0.1%
294524 1
< 0.1%
370554 1
< 0.1%
1099912 1
< 0.1%
1117091 1
< 0.1%
ValueCountFrequency (%)
6241793 1
< 0.1%
6241781 1
< 0.1%
6221971 1
< 0.1%
6174991 1
< 0.1%
6173091 1
< 0.1%
6142352 1
< 0.1%
6142342 1
< 0.1%
6133572 1
< 0.1%
6132894 1
< 0.1%
6132891 1
< 0.1%

공사관리번호
Text

Distinct1333
Distinct (%)13.4%
Missing60
Missing (%)0.6%
Memory size156.2 KiB
2024-05-03T21:54:44.038775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters129220
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique502 ?
Unique (%)5.1%

Sample

1st row2000-0000-000
2nd row2000-0000-000
3rd row2000-0000-000
4th row2000-0000-000
5th row2000-0000-000
ValueCountFrequency (%)
2000-0000-000 2557
25.7%
2008-0201-006 249
 
2.5%
2008-0101-025 224
 
2.3%
2008-0201-004 183
 
1.8%
2009-0201-003 182
 
1.8%
2008-0201-005 150
 
1.5%
2008-0101-911 148
 
1.5%
2007-0201-457 103
 
1.0%
2007-0201-663 102
 
1.0%
2008-0101-017 99
 
1.0%
Other values (1323) 5943
59.8%
2024-05-03T21:54:45.104892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 60008
46.4%
- 19876
 
15.4%
1 19236
 
14.9%
2 14000
 
10.8%
8 3218
 
2.5%
4 2743
 
2.1%
9 2449
 
1.9%
5 2157
 
1.7%
7 1895
 
1.5%
6 1811
 
1.4%
Other values (5) 1827
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 109318
84.6%
Dash Punctuation 19876
 
15.4%
Space Separator 18
 
< 0.1%
Lowercase Letter 8
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 60008
54.9%
1 19236
 
17.6%
2 14000
 
12.8%
8 3218
 
2.9%
4 2743
 
2.5%
9 2449
 
2.2%
5 2157
 
2.0%
7 1895
 
1.7%
6 1811
 
1.7%
3 1801
 
1.6%
Lowercase Letter
ValueCountFrequency (%)
l 4
50.0%
n 2
25.0%
u 2
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 19876
100.0%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 129212
> 99.9%
Latin 8
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 60008
46.4%
- 19876
 
15.4%
1 19236
 
14.9%
2 14000
 
10.8%
8 3218
 
2.5%
4 2743
 
2.1%
9 2449
 
1.9%
5 2157
 
1.7%
7 1895
 
1.5%
6 1811
 
1.4%
Other values (2) 1819
 
1.4%
Latin
ValueCountFrequency (%)
l 4
50.0%
n 2
25.0%
u 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 60008
46.4%
- 19876
 
15.4%
1 19236
 
14.9%
2 14000
 
10.8%
8 3218
 
2.5%
4 2743
 
2.1%
9 2449
 
1.9%
5 2157
 
1.7%
7 1895
 
1.5%
6 1811
 
1.4%
Other values (5) 1827
 
1.4%

부착대관리번호.1
Text

Distinct9253
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T21:54:46.084468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters90000
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

Unique8548 ?
Unique (%)85.5%

Sample

1st row03-014821
2nd row03-050443
3rd row03-009436
4th row03-044463
5th row03-026881
ValueCountFrequency (%)
03-018942 4
 
< 0.1%
03-014203 4
 
< 0.1%
03-005623 4
 
< 0.1%
03-057579 4
 
< 0.1%
03-005064 4
 
< 0.1%
03-049511 3
 
< 0.1%
03-044086 3
 
< 0.1%
03-017786 3
 
< 0.1%
03-000290 3
 
< 0.1%
03-007053 3
 
< 0.1%
Other values (9243) 9965
99.7%
2024-05-03T21:54:48.100924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26141
29.0%
3 15188
16.9%
- 10000
 
11.1%
1 5946
 
6.6%
5 5746
 
6.4%
2 5526
 
6.1%
4 5343
 
5.9%
6 4346
 
4.8%
7 4124
 
4.6%
8 3929
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80000
88.9%
Dash Punctuation 10000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26141
32.7%
3 15188
19.0%
1 5946
 
7.4%
5 5746
 
7.2%
2 5526
 
6.9%
4 5343
 
6.7%
6 4346
 
5.4%
7 4124
 
5.2%
8 3929
 
4.9%
9 3711
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26141
29.0%
3 15188
16.9%
- 10000
 
11.1%
1 5946
 
6.6%
5 5746
 
6.4%
2 5526
 
6.1%
4 5343
 
5.9%
6 4346
 
4.8%
7 4124
 
4.6%
8 3929
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26141
29.0%
3 15188
16.9%
- 10000
 
11.1%
1 5946
 
6.6%
5 5746
 
6.4%
2 5526
 
6.1%
4 5343
 
5.9%
6 4346
 
4.8%
7 4124
 
4.6%
8 3929
 
4.4%

이력ID
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33531.823
Minimum3
Maximum115752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T21:54:48.736073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile2996.95
Q115486
median30501
Q345497.75
95-th percentile92345.15
Maximum115752
Range115749
Interquartile range (IQR)30011.75

Descriptive statistics

Standard deviation24378.23
Coefficient of variation (CV)0.72701774
Kurtosis1.2493607
Mean33531.823
Median Absolute Deviation (MAD)15014
Skewness1.1560349
Sum3.3531823 × 108
Variance5.942981 × 108
MonotonicityNot monotonic
2024-05-03T21:54:49.303975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51914 1
 
< 0.1%
24757 1
 
< 0.1%
19386 1
 
< 0.1%
12938 1
 
< 0.1%
17173 1
 
< 0.1%
15810 1
 
< 0.1%
91524 1
 
< 0.1%
104672 1
 
< 0.1%
40431 1
 
< 0.1%
34690 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
3 1
< 0.1%
15 1
< 0.1%
20 1
< 0.1%
31 1
< 0.1%
40 1
< 0.1%
45 1
< 0.1%
48 1
< 0.1%
49 1
< 0.1%
51 1
< 0.1%
52 1
< 0.1%
ValueCountFrequency (%)
115752 1
< 0.1%
115738 1
< 0.1%
115285 1
< 0.1%
115164 1
< 0.1%
115104 1
< 0.1%
114957 1
< 0.1%
114608 1
< 0.1%
113720 1
< 0.1%
113514 1
< 0.1%
112957 1
< 0.1%

위치정보
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

X좌표
Real number (ℝ)

Distinct8768
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199006.9
Minimum182175.89
Maximum216141.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T21:54:50.069594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182175.89
5-th percentile186728.93
Q1192794.02
median199708.2
Q3204876.76
95-th percentile211384.85
Maximum216141.95
Range33966.06
Interquartile range (IQR)12082.736

Descriptive statistics

Standard deviation7535.606
Coefficient of variation (CV)0.037866054
Kurtosis-0.89761617
Mean199006.9
Median Absolute Deviation (MAD)6229.2779
Skewness-0.02861211
Sum1.990069 × 109
Variance56785358
MonotonicityNot monotonic
2024-05-03T21:54:50.736711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201306.47594 7
 
0.1%
199172.29789 6
 
0.1%
206667.44328 5
 
0.1%
205878.36695 5
 
0.1%
201799.38871 5
 
0.1%
201169.09589 5
 
0.1%
206766.44375 5
 
0.1%
198464.7375 5
 
0.1%
189268.86238 5
 
0.1%
202464.73841 4
 
< 0.1%
Other values (8758) 9948
99.5%
ValueCountFrequency (%)
182175.89063 1
< 0.1%
182225.64809 1
< 0.1%
182249.63595 1
< 0.1%
182288.21848 1
< 0.1%
182316.75689 1
< 0.1%
182430.06412 1
< 0.1%
182451.07552 1
< 0.1%
182452.4872 1
< 0.1%
182470.47491 1
< 0.1%
182494.51888 1
< 0.1%
ValueCountFrequency (%)
216141.95075 2
< 0.1%
215894.22428 1
< 0.1%
215851.49028 1
< 0.1%
215833.20754 1
< 0.1%
215717.94216 1
< 0.1%
215644.1289 1
< 0.1%
215571.39832 1
< 0.1%
215474.73884 1
< 0.1%
215405.55363 1
< 0.1%
215370.80996 1
< 0.1%

Y좌표
Real number (ℝ)

Distinct8764
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean550218.07
Minimum537164.36
Maximum565545.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T21:54:51.451591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum537164.36
5-th percentile541900.72
Q1545381.23
median549977.38
Q3554070.31
95-th percentile561047.21
Maximum565545.53
Range28381.169
Interquartile range (IQR)8689.0734

Descriptive statistics

Standard deviation5886.1969
Coefficient of variation (CV)0.010697935
Kurtosis-0.64393769
Mean550218.07
Median Absolute Deviation (MAD)4387.6006
Skewness0.32134185
Sum5.5021807 × 109
Variance34647314
MonotonicityNot monotonic
2024-05-03T21:54:52.045025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
551162.50697 7
 
0.1%
551319.86897 6
 
0.1%
557140.07831 5
 
0.1%
556596.34823 5
 
0.1%
552180.46222 5
 
0.1%
545245.98193 5
 
0.1%
556025.20593 5
 
0.1%
545574.40239 5
 
0.1%
553034.86886 5
 
0.1%
553988.26431 4
 
< 0.1%
Other values (8754) 9948
99.5%
ValueCountFrequency (%)
537164.35967 1
< 0.1%
537175.26822 1
< 0.1%
537253.93102 1
< 0.1%
537264.14999 1
< 0.1%
537395.11586 2
< 0.1%
537594.11397 1
< 0.1%
537603.25978 1
< 0.1%
537681.68255 2
< 0.1%
537690.31612 1
< 0.1%
537696.23883 1
< 0.1%
ValueCountFrequency (%)
565545.5289 2
< 0.1%
565542.26496 1
< 0.1%
565504.03612 1
< 0.1%
565347.44844 1
< 0.1%
565310.19113 1
< 0.1%
565284.90701 1
< 0.1%
565284.01565 1
< 0.1%
565268.82413 1
< 0.1%
565182.26234 1
< 0.1%
565181.4506 2
< 0.1%

공사형태 (공통)
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9650 
3
 
170
1
 
83
4
 
68
6
 
18

Length

Max length4
Median length4
Mean length3.895
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9650
96.5%
3 170
 
1.7%
1 83
 
0.8%
4 68
 
0.7%
6 18
 
0.2%
2 11
 
0.1%

Length

2024-05-03T21:54:52.699348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T21:54:53.093338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9650
96.5%
3 170
 
1.7%
1 83
 
0.8%
4 68
 
0.7%
6 18
 
0.2%
2 11
 
0.1%

Sample

부착대관리번호상태 (공통)부착방식부착대길이고가 (공통)부착대방향신호등수량배면등수량신호등종류배면등종류설치일교체일지주관리번호신호발광구분제조회사작업구분 (공통)표출구분 (공통)신규정규화ID공사관리번호부착대관리번호.1이력ID위치정보X좌표Y좌표공사형태 (공통)
4878703-00000148211299912591010<NA><NA><NA>02-00000453492<NA>11<NA>2000-0000-00003-01482151914<NA>212284.34287545032.96773<NA>
565703-00000504431361842021<NA><NA>02-00001529072<NA>21<NA>2000-0000-00003-0504433987<NA>192257.80277544393.0221<NA>
1763503-000000943613512302122<NA><NA>02-00000959592<NA>41<NA>2000-0000-00003-00943616016<NA>197356.05656552833.34997<NA>
163403-00000444631331312011<NA><NA>02-00001321781<NA>41<NA>2000-0000-00003-04446393784<NA>197765.67308543307.81754<NA>
3305303-000002688112111681011<NA><NA>02-00001035651021<NA>2000-0000-00003-02688133231<NA>205152.28231562386.3773<NA>
366003-0000050052129991204107<NA><NA><NA>02-00000838822.41<NA>2007-1301-48203-0500521389<NA>203036.75255557058.41055<NA>
4407803-0000005729129991291107<NA><NA><NA>02-00000175542<NA>41<NA>2008-0201-00703-00572940646<NA>198174.94266542796.07979<NA>
1389403-000001167213713011133<NA><NA>02-00000272972<NA>12<NA>2000-0000-00003-01167210248<NA>189724.96875540609.0625<NA>
3963403-0000001707129991133107<NA><NA><NA>02-00000160561<NA>41<NA>2008-1201-71903-00170742749<NA>211992.88645543643.86042<NA>
4082503-00000124881371291103<NA><NA><NA>02-00000342852<NA>12<NA>2009-1101-05703-01248840214<NA>201142.15625545454.78125<NA>
부착대관리번호상태 (공통)부착방식부착대길이고가 (공통)부착대방향신호등수량배면등수량신호등종류배면등종류설치일교체일지주관리번호신호발광구분제조회사작업구분 (공통)표출구분 (공통)신규정규화ID공사관리번호부착대관리번호.1이력ID위치정보X좌표Y좌표공사형태 (공통)
3729703-000004513213711771112<NA><NA>02-00001332532021<NA>2004-1101-22303-04513230676<NA>197873.30111551143.75121<NA>
2306303-000003145613712522222<NA><NA>02-00001026411<NA>41<NA>2008-0112-83403-03145617254<NA>200135.49938556479.92324<NA>
3707403-0000035096129991357107<NA><NA><NA>02-00000490762<NA>11<NA>2000-0000-00003-03509634114<NA>212749.6144550561.71478<NA>
2149703-000005101513511902022<NA><NA>02-00001328551<NA>41<NA>2000-0000-00003-05101518792<NA>201528.19736549113.09904<NA>
1587603-000000314412999163107<NA><NA><NA>02-00000867842<NA>41<NA>2000-0000-00003-00314414012<NA>204200.29827562390.98158<NA>
1279003-000000092112<NA>151107<NA><NA><NA>02-00000029092<NA>12<NA>2000-0000-00003-0009214448<NA>195100.62795549503.81502<NA>
214303-0000066287129991148107<NA>201407042014070402-00001792002<NA>125166252<NA>03-066287104361<NA>207823.9375545031.531251
3447703-000002422612999195107<NA><NA><NA>02-00000823422<NA>41<NA>2000-0000-00003-02422631974<NA>207195.48546555780.14613<NA>
434003-0000050260129991260107<NA><NA><NA>02-00001396852041<NA>2000-0000-00003-05026055105<NA>203376.10411552585.85573<NA>
1848203-0000043187139991259107<NA><NA><NA>02-00001279942<NA>12<NA>2000-0000-00003-04318715750<NA>190371.89228546694.46092<NA>