Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells41840
Missing cells (%)26.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory146.0 B

Variable types

Numeric7
Text2
Categorical4
DateTime3

Dataset

Description인천광역시 버스정보시스템 서버 관련 데이터 파일로서 버스정보안내기에 정보를 표출하기 위한 노드 현황 자료입니다. 노드링크 체계 상에 규정된 레벨1~15(물리적) 노드 + 정류소, 차고지 노드로 구성되어 있는 파일입니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15117308&srcSe=7661IVAWM27C61E190

Alerts

노드구분코드 is highly overall correlated with 노드 관리번호(ID) and 3 other fieldsHigh correlation
비고 is highly overall correlated with 노드유형코드 and 1 other fieldsHigh correlation
노드 관리번호(ID) is highly overall correlated with 노드유형코드 and 1 other fieldsHigh correlation
노드유형코드 is highly overall correlated with 노드 관리번호(ID) and 1 other fieldsHigh correlation
접근로개수 is highly overall correlated with 검지범위 and 1 other fieldsHigh correlation
검지범위 is highly overall correlated with 접근로개수 and 1 other fieldsHigh correlation
비고 is highly imbalanced (83.5%)Imbalance
사용여부 is highly imbalanced (70.6%)Imbalance
노드명 has 684 (6.8%) missing valuesMissing
접근로개수 has 8048 (80.5%) missing valuesMissing
적용 시작일 has 7069 (70.7%) missing valuesMissing
적용 시작 시간 has 7069 (70.7%) missing valuesMissing
적용 종료일 has 9485 (94.8%) missing valuesMissing
적용 종료 시간 has 9485 (94.8%) missing valuesMissing
노드 관리번호(ID) has unique valuesUnique
노드유형코드 has 943 (9.4%) zerosZeros
접근로개수 has 120 (1.2%) zerosZeros

Reproduction

Analysis started2024-01-28 15:22:59.042958
Analysis finished2024-01-28 15:23:05.156483
Duration6.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노드 관리번호(ID)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7894707 × 108
Minimum1
Maximum2.3500021 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T00:23:05.216950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.1995002 × 108
Q11.6500098 × 108
median1.7000001 × 108
Q31.640047 × 109
95-th percentile2.1800006 × 109
Maximum2.3500021 × 109
Range2.3500021 × 109
Interquartile range (IQR)1.4750461 × 109

Descriptive statistics

Standard deviation7.7847522 × 108
Coefficient of variation (CV)0.99939424
Kurtosis-1.3440958
Mean7.7894707 × 108
Median Absolute Deviation (MAD)49000040
Skewness0.62610941
Sum7.7894707 × 1012
Variance6.0602366 × 1017
MonotonicityNot monotonic
2024-01-29T00:23:05.344332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1690000900 1
 
< 0.1%
1680079700 1
 
< 0.1%
2120009100 1
 
< 0.1%
1640035200 1
 
< 0.1%
166000675 1
 
< 0.1%
167000558 1
 
< 0.1%
167000468 1
 
< 0.1%
168000441 1
 
< 0.1%
2240020200 1
 
< 0.1%
1620005200 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
99999999 1
< 0.1%
100000001 1
< 0.1%
100000003 1
< 0.1%
100000006 1
< 0.1%
100000009 1
< 0.1%
100000013 1
< 0.1%
100000014 1
< 0.1%
100000018 1
< 0.1%
100000021 1
< 0.1%
ValueCountFrequency (%)
2350002100 1
< 0.1%
2350002000 1
< 0.1%
2350001700 1
< 0.1%
2350001400 1
< 0.1%
2350001300 1
< 0.1%
2350001200 1
< 0.1%
2350001100 1
< 0.1%
2350000900 1
< 0.1%
2350000800 1
< 0.1%
2350000700 1
< 0.1%

노드명
Text

MISSING 

Distinct4935
Distinct (%)53.0%
Missing684
Missing (%)6.8%
Memory size156.2 KiB
2024-01-29T00:23:05.607362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length5.6407256
Min length1

Characters and Unicode

Total characters52549
Distinct characters667
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3507 ?
Unique (%)37.6%

Sample

1st row호국교육원입구
2nd row생성노드
3rd row생성노드
4th row아인스월드
5th row없음
ValueCountFrequency (%)
생성노드 1636
 
17.4%
없음 716
 
7.6%
90
 
1.0%
대동아파트 15
 
0.2%
금호아파트 14
 
0.1%
현대아파트 13
 
0.1%
풍림아파트 13
 
0.1%
신동아아파트 9
 
0.1%
제물포역 9
 
0.1%
뉴서울아파트 9
 
0.1%
Other values (4955) 6874
73.1%
2024-01-29T00:23:06.010206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
성 1862
 
3.5%
드 1722
 
3.3%
노 1681
 
3.2%
생 1663
 
3.2%
리 1073
 
2.0%
아 996
 
1.9%
교 944
 
1.8%
트 932
 
1.8%
동 889
 
1.7%
파 861
 
1.6%
Other values (657) 39926
76.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48907
93.1%
Decimal Number 1229
 
2.3%
Uppercase Letter 683
 
1.3%
Open Punctuation 555
 
1.1%
Close Punctuation 552
 
1.1%
Other Punctuation 395
 
0.8%
Dash Punctuation 110
 
0.2%
Space Separator 83
 
0.2%
Lowercase Letter 33
 
0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 1862
 
3.8%
드 1722
 
3.5%
노 1681
 
3.4%
생 1663
 
3.4%
리 1073
 
2.2%
아 996
 
2.0%
교 944
 
1.9%
트 932
 
1.9%
동 889
 
1.8%
파 861
 
1.8%
Other values (606) 36284
74.2%
Uppercase Letter
ValueCountFrequency (%)
C 192
28.1%
I 141
20.6%
S 53
 
7.8%
T 48
 
7.0%
J 46
 
6.7%
K 44
 
6.4%
G 36
 
5.3%
L 27
 
4.0%
A 21
 
3.1%
B 19
 
2.8%
Other values (12) 56
 
8.2%
Decimal Number
ValueCountFrequency (%)
1 351
28.6%
2 263
21.4%
3 177
14.4%
0 85
 
6.9%
5 80
 
6.5%
6 79
 
6.4%
4 75
 
6.1%
8 44
 
3.6%
7 41
 
3.3%
9 34
 
2.8%
Lowercase Letter
ValueCountFrequency (%)
e 11
33.3%
s 4
 
12.1%
d 3
 
9.1%
t 3
 
9.1%
i 2
 
6.1%
n 2
 
6.1%
f 2
 
6.1%
m 2
 
6.1%
y 2
 
6.1%
g 2
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 385
97.5%
/ 7
 
1.8%
· 2
 
0.5%
& 1
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 555
100.0%
Close Punctuation
ValueCountFrequency (%)
) 552
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 110
100.0%
Space Separator
ValueCountFrequency (%)
83
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48907
93.1%
Common 2926
 
5.6%
Latin 716
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 1862
 
3.8%
드 1722
 
3.5%
노 1681
 
3.4%
생 1663
 
3.4%
리 1073
 
2.2%
아 996
 
2.0%
교 944
 
1.9%
트 932
 
1.9%
동 889
 
1.8%
파 861
 
1.8%
Other values (606) 36284
74.2%
Latin
ValueCountFrequency (%)
C 192
26.8%
I 141
19.7%
S 53
 
7.4%
T 48
 
6.7%
J 46
 
6.4%
K 44
 
6.1%
G 36
 
5.0%
L 27
 
3.8%
A 21
 
2.9%
B 19
 
2.7%
Other values (22) 89
12.4%
Common
ValueCountFrequency (%)
( 555
19.0%
) 552
18.9%
. 385
13.2%
1 351
12.0%
2 263
9.0%
3 177
 
6.0%
- 110
 
3.8%
0 85
 
2.9%
83
 
2.8%
5 80
 
2.7%
Other values (9) 285
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48907
93.1%
ASCII 3640
 
6.9%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
성 1862
 
3.8%
드 1722
 
3.5%
노 1681
 
3.4%
생 1663
 
3.4%
리 1073
 
2.2%
아 996
 
2.0%
교 944
 
1.9%
트 932
 
1.9%
동 889
 
1.8%
파 861
 
1.8%
Other values (606) 36284
74.2%
ASCII
ValueCountFrequency (%)
( 555
15.2%
) 552
15.2%
. 385
10.6%
1 351
9.6%
2 263
 
7.2%
C 192
 
5.3%
3 177
 
4.9%
I 141
 
3.9%
- 110
 
3.0%
0 85
 
2.3%
Other values (40) 829
22.8%
None
ValueCountFrequency (%)
· 2
100.0%

노드구분코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
6015 
1
3985 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 6015
60.2%
1 3985
39.9%

Length

2024-01-29T00:23:06.112517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T00:23:06.180285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 6015
60.2%
1 3985
39.9%

권역코드 행자부 시군구 권역코드
Real number (ℝ)

Distinct61
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.4283
Minimum0
Maximum999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T00:23:06.264935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile114
Q1163
median166
Q3169
95-th percentile224
Maximum999
Range999
Interquartile range (IQR)6

Descriptive statistics

Standard deviation31.495683
Coefficient of variation (CV)0.18589387
Kurtosis48.290358
Mean169.4283
Median Absolute Deviation (MAD)3
Skewness1.9804857
Sum1694283
Variance991.97806
MonotonicityNot monotonic
2024-01-29T00:23:06.370074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
168 1230
12.3%
169 1095
 
10.9%
165 903
 
9.0%
161 885
 
8.8%
166 715
 
7.1%
164 684
 
6.8%
167 526
 
5.3%
163 498
 
5.0%
232 371
 
3.7%
210 314
 
3.1%
Other values (51) 2779
27.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
100 52
0.5%
101 37
0.4%
102 55
0.5%
103 24
0.2%
104 10
 
0.1%
105 29
0.3%
106 15
 
0.1%
107 27
0.3%
108 9
 
0.1%
ValueCountFrequency (%)
999 1
 
< 0.1%
277 22
 
0.2%
235 20
 
0.2%
232 371
3.7%
229 50
 
0.5%
226 10
 
0.1%
225 17
 
0.2%
224 314
3.1%
220 63
 
0.6%
219 103
 
1.0%

노드유형코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.3555
Minimum0
Maximum108
Zeros943
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T00:23:06.458698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1103
median103
Q3103
95-th percentile104
Maximum108
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30.134803
Coefficient of variation (CV)0.32279622
Kurtosis5.7009632
Mean93.3555
Median Absolute Deviation (MAD)0
Skewness-2.7734732
Sum933555
Variance908.10633
MonotonicityNot monotonic
2024-01-29T00:23:06.539627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
103 6133
61.3%
104 2069
 
20.7%
0 943
 
9.4%
101 770
 
7.7%
102 36
 
0.4%
108 31
 
0.3%
105 18
 
0.2%
ValueCountFrequency (%)
0 943
 
9.4%
101 770
 
7.7%
102 36
 
0.4%
103 6133
61.3%
104 2069
 
20.7%
105 18
 
0.2%
108 31
 
0.3%
ValueCountFrequency (%)
108 31
 
0.3%
105 18
 
0.2%
104 2069
 
20.7%
103 6133
61.3%
102 36
 
0.4%
101 770
 
7.7%
0 943
 
9.4%

회전제한유무
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
7811 
<NA>
2017 
1
 
172

Length

Max length4
Median length1
Mean length1.6051
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 7811
78.1%
<NA> 2017
 
20.2%
1 172
 
1.7%

Length

2024-01-29T00:23:06.627726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T00:23:06.708728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7811
78.1%
na 2017
 
20.2%
1 172
 
1.7%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8752 
보완
 
733
0
 
221
1
 
134
신규
 
46
Other values (24)
 
114

Length

Max length23
Median length4
Mean length3.7542
Min length1

Unique

Unique8 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8752
87.5%
보완 733
 
7.3%
0 221
 
2.2%
1 134
 
1.3%
신규 46
 
0.5%
시스템노드 25
 
0.2%
구축외 24
 
0.2%
2 11
 
0.1%
sa_tg_add 6
 
0.1%
서울시계 6
 
0.1%
Other values (19) 42
 
0.4%

Length

2024-01-29T00:23:06.795182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 8752
87.3%
보완 733
 
7.3%
0 221
 
2.2%
1 134
 
1.3%
신규 46
 
0.5%
시스템노드 25
 
0.2%
구축외 24
 
0.2%
2 11
 
0.1%
교차로 9
 
0.1%
서울시계 6
 
0.1%
Other values (29) 68
 
0.7%

X좌표
Real number (ℝ)

Distinct9887
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172995.99
Minimum131125.16
Maximum215339.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T00:23:06.888739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum131125.16
5-th percentile148433.38
Q1167068.53
median173003.67
Q3178812.3
95-th percentile199856.17
Maximum215339.39
Range84214.229
Interquartile range (IQR)11743.77

Descriptive statistics

Standard deviation14202.191
Coefficient of variation (CV)0.08209549
Kurtosis0.28692176
Mean172995.99
Median Absolute Deviation (MAD)5902.6959
Skewness0.13947077
Sum1.7299599 × 109
Variance2.0170223 × 108
MonotonicityNot monotonic
2024-01-29T00:23:06.985309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184001.8826 3
 
< 0.1%
193265.8058 2
 
< 0.1%
195585.5919 2
 
< 0.1%
180957.0112 2
 
< 0.1%
180236.4435 2
 
< 0.1%
181143.4756 2
 
< 0.1%
179619.6973 2
 
< 0.1%
195906.1096 2
 
< 0.1%
180799.0966 2
 
< 0.1%
186819.5 2
 
< 0.1%
Other values (9877) 9979
99.8%
ValueCountFrequency (%)
131125.1577 1
< 0.1%
131132.1292 1
< 0.1%
131802.9246 1
< 0.1%
132116.7032 1
< 0.1%
132295.7484 1
< 0.1%
132302.435 1
< 0.1%
132421.6376 1
< 0.1%
132447.9843 1
< 0.1%
132453.5692 1
< 0.1%
132485.1537 1
< 0.1%
ValueCountFrequency (%)
215339.3868 1
< 0.1%
215235.1185 1
< 0.1%
215150.7757 1
< 0.1%
213797.6477 1
< 0.1%
213782.2863 1
< 0.1%
213775.7 1
< 0.1%
213071.9 1
< 0.1%
212761.7653 1
< 0.1%
212606.3 1
< 0.1%
212570.6 1
< 0.1%

Y좌표
Real number (ℝ)

Distinct9888
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446358.57
Minimum412303.54
Maximum484503.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T00:23:07.084561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum412303.54
5-th percentile430984.43
Q1438693.53
median444799.81
Q3452230.45
95-th percentile470134.45
Maximum484503.05
Range72199.51
Interquartile range (IQR)13536.922

Descriptive statistics

Standard deviation11857.313
Coefficient of variation (CV)0.026564545
Kurtosis0.49298211
Mean446358.57
Median Absolute Deviation (MAD)6619.1768
Skewness0.50079339
Sum4.4635857 × 109
Variance1.4059586 × 108
MonotonicityNot monotonic
2024-01-29T00:23:07.196580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
453567.3466 3
 
< 0.1%
444708.2996 2
 
< 0.1%
447780.2783 2
 
< 0.1%
438373.7135 2
 
< 0.1%
446117.2004 2
 
< 0.1%
450546.2966 2
 
< 0.1%
437672.3201 2
 
< 0.1%
451563.169 2
 
< 0.1%
447071.7581 2
 
< 0.1%
451675.4331 2
 
< 0.1%
Other values (9878) 9979
99.8%
ValueCountFrequency (%)
412303.5437 1
< 0.1%
412445.3429 1
< 0.1%
412566.9304 1
< 0.1%
413025.7883 1
< 0.1%
413736.3934 1
< 0.1%
413833.952 2
< 0.1%
413859.8791 1
< 0.1%
413868.2322 1
< 0.1%
413879.0359 1
< 0.1%
414377.1818 1
< 0.1%
ValueCountFrequency (%)
484503.0533 1
< 0.1%
484300.6279 1
< 0.1%
484275.2167 1
< 0.1%
484269.2167 1
< 0.1%
483828.4068 2
< 0.1%
483812.5432 2
< 0.1%
483719.2167 1
< 0.1%
483714.2167 1
< 0.1%
483608.7167 1
< 0.1%
483541.3639 1
< 0.1%

접근로개수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)0.6%
Missing8048
Missing (%)80.5%
Infinite0
Infinite (%)0.0%
Mean2.2428279
Minimum0
Maximum16
Zeros120
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T00:23:07.293407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q32
95-th percentile4
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3757994
Coefficient of variation (CV)0.61342175
Kurtosis11.938755
Mean2.2428279
Median Absolute Deviation (MAD)0
Skewness2.4635749
Sum4378
Variance1.892824
MonotonicityNot monotonic
2024-01-29T00:23:07.386078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 1243
 
12.4%
1 187
 
1.9%
3 157
 
1.6%
4 150
 
1.5%
0 120
 
1.2%
5 30
 
0.3%
6 28
 
0.3%
8 27
 
0.3%
10 7
 
0.1%
7 2
 
< 0.1%
(Missing) 8048
80.5%
ValueCountFrequency (%)
0 120
 
1.2%
1 187
 
1.9%
2 1243
12.4%
3 157
 
1.6%
4 150
 
1.5%
5 30
 
0.3%
6 28
 
0.3%
7 2
 
< 0.1%
8 27
 
0.3%
10 7
 
0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
10 7
 
0.1%
8 27
 
0.3%
7 2
 
< 0.1%
6 28
 
0.3%
5 30
 
0.3%
4 150
 
1.5%
3 157
 
1.6%
2 1243
12.4%
1 187
 
1.9%

적용 시작일
Date

MISSING 

Distinct495
Distinct (%)16.9%
Missing7069
Missing (%)70.7%
Memory size156.2 KiB
Minimum2004-11-17 00:00:00
Maximum2020-08-12 00:00:00
2024-01-29T00:23:07.490801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:07.602774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

적용 시작 시간
Date

MISSING 

Distinct313
Distinct (%)10.7%
Missing7069
Missing (%)70.7%
Memory size156.2 KiB
Minimum2024-01-29 00:00:00
Maximum2024-01-29 22:02:28
2024-01-29T00:23:07.717224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:07.824853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

적용 종료일
Text

MISSING 

Distinct170
Distinct (%)33.0%
Missing9485
Missing (%)94.8%
Memory size156.2 KiB
2024-01-29T00:23:08.087302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5150
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

Unique98 ?
Unique (%)19.0%

Sample

1st row2007-10-06
2nd row2012-06-15
3rd row2014-10-14
4th row2007-10-06
5th row2010-06-17
ValueCountFrequency (%)
2007-10-06 176
34.2%
2014-10-14 13
 
2.5%
2018-01-26 11
 
2.1%
2010-06-17 10
 
1.9%
2013-14-23 8
 
1.6%
2010-01-11 8
 
1.6%
2007-11-06 8
 
1.6%
2011-06-23 8
 
1.6%
2009-04-23 7
 
1.4%
2007-11-13 6
 
1.2%
Other values (160) 260
50.5%
2024-01-29T00:23:08.471607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1564
30.4%
- 1030
20.0%
2 772
15.0%
1 754
14.6%
6 285
 
5.5%
7 267
 
5.2%
3 149
 
2.9%
4 125
 
2.4%
8 87
 
1.7%
9 80
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4120
80.0%
Dash Punctuation 1030
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1564
38.0%
2 772
18.7%
1 754
18.3%
6 285
 
6.9%
7 267
 
6.5%
3 149
 
3.6%
4 125
 
3.0%
8 87
 
2.1%
9 80
 
1.9%
5 37
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 1030
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1564
30.4%
- 1030
20.0%
2 772
15.0%
1 754
14.6%
6 285
 
5.5%
7 267
 
5.2%
3 149
 
2.9%
4 125
 
2.4%
8 87
 
1.7%
9 80
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1564
30.4%
- 1030
20.0%
2 772
15.0%
1 754
14.6%
6 285
 
5.5%
7 267
 
5.2%
3 149
 
2.9%
4 125
 
2.4%
8 87
 
1.7%
9 80
 
1.6%

적용 종료 시간
Date

MISSING 

Distinct98
Distinct (%)19.0%
Missing9485
Missing (%)94.8%
Memory size156.2 KiB
Minimum2024-01-29 00:00:00
Maximum2024-01-29 22:27:00
2024-01-29T00:23:08.586468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:08.698725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사용여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9481 
0
 
519

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 9481
94.8%
0 519
 
5.2%

Length

2024-01-29T00:23:08.811128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T00:23:08.887601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9481
94.8%
0 519
 
5.2%

검지범위
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.9474
Minimum5
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T00:23:08.947266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile20
Q130
median30
Q330
95-th percentile30
Maximum80
Range75
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.5811959
Coefficient of variation (CV)0.19280474
Kurtosis23.848355
Mean28.9474
Median Absolute Deviation (MAD)0
Skewness1.6960259
Sum289474
Variance31.149748
MonotonicityNot monotonic
2024-01-29T00:23:09.026818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
30 8380
83.8%
20 1131
 
11.3%
10 201
 
2.0%
40 198
 
2.0%
50 32
 
0.3%
80 28
 
0.3%
60 26
 
0.3%
5 2
 
< 0.1%
44 1
 
< 0.1%
70 1
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
10 201
 
2.0%
20 1131
 
11.3%
30 8380
83.8%
40 198
 
2.0%
44 1
 
< 0.1%
50 32
 
0.3%
60 26
 
0.3%
70 1
 
< 0.1%
80 28
 
0.3%
ValueCountFrequency (%)
80 28
 
0.3%
70 1
 
< 0.1%
60 26
 
0.3%
50 32
 
0.3%
44 1
 
< 0.1%
40 198
 
2.0%
30 8380
83.8%
20 1131
 
11.3%
10 201
 
2.0%
5 2
 
< 0.1%

Interactions

2024-01-29T00:23:03.942245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:00.576593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:01.090736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:01.642765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:02.268279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:02.799611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:03.360900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:04.014183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:00.643168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:01.168478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:01.723988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:02.339202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:02.881750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:03.448876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:04.083627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:00.713397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:01.242804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:01.815161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:02.407459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:02.958717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:03.550921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:04.158730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:00.787037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:01.321549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:01.919468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:02.471513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:03.036291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:03.626578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:04.234154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:00.851429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:01.390051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:02.015117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:02.542371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:03.113305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:03.704459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:04.322047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:00.925436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:01.473836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:02.108368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:02.622611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:03.198250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:03.782701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:04.406423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:01.005005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:01.560282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:02.188308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:02.704537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:03.278360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:23:03.865550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T00:23:09.098549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드 관리번호(ID)노드구분코드권역코드 행자부 시군구 권역코드노드유형코드회전제한유무비고X좌표Y좌표접근로개수적용 종료 시간사용여부검지범위
노드 관리번호(ID)1.0001.0000.7310.6000.4590.8150.5320.3810.2990.9500.0840.646
노드구분코드1.0001.0000.1510.5830.2291.0000.1880.118NaN0.9900.0000.664
권역코드 행자부 시군구 권역코드0.7310.1511.0000.1580.1520.4820.3780.3130.2010.8470.0710.269
노드유형코드0.6000.5830.1581.0000.081NaN0.3440.1740.5860.9760.1980.910
회전제한유무0.4590.2290.1520.0811.0000.0810.1790.1770.1540.6070.0000.278
비고0.8151.0000.482NaN0.0811.0000.4550.2010.0000.8150.1710.562
X좌표0.5320.1880.3780.3440.1790.4551.0000.6710.2960.9310.1190.304
Y좌표0.3810.1180.3130.1740.1770.2010.6711.0000.2170.8550.1060.135
접근로개수0.299NaN0.2010.5860.1540.0000.2960.2171.0000.6850.0800.969
적용 종료 시간0.9500.9900.8470.9760.6070.8150.9310.8550.6851.0000.0000.925
사용여부0.0840.0000.0710.1980.0000.1710.1190.1060.0800.0001.0000.142
검지범위0.6460.6640.2690.9100.2780.5620.3040.1350.9690.9250.1421.000
2024-01-29T00:23:09.428262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드구분코드비고회전제한유무사용여부
노드구분코드1.0000.9900.1470.000
비고0.9901.0000.0690.134
회전제한유무0.1470.0691.0000.000
사용여부0.0000.1340.0001.000
2024-01-29T00:23:09.505636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드 관리번호(ID)권역코드 행자부 시군구 권역코드노드유형코드X좌표Y좌표접근로개수검지범위노드구분코드회전제한유무비고사용여부
노드 관리번호(ID)1.0000.497-0.5070.0130.071-0.122-0.3561.0000.3450.4590.063
권역코드 행자부 시군구 권역코드0.4971.000-0.009-0.1240.228-0.115-0.1090.1000.2510.3800.047
노드유형코드-0.507-0.0091.0000.0230.0100.2240.4800.3960.0521.0000.127
X좌표0.013-0.1240.0231.000-0.1150.200-0.0840.1440.1370.1920.091
Y좌표0.0710.2280.010-0.1151.000-0.002-0.0170.0900.1350.0730.081
접근로개수-0.122-0.1150.2240.200-0.0021.0000.6331.0000.1040.0000.064
검지범위-0.356-0.1090.480-0.084-0.0170.6331.0000.5050.2090.2450.106
노드구분코드1.0000.1000.3960.1440.0901.0000.5051.0000.1470.9900.000
회전제한유무0.3450.2510.0520.1370.1350.1040.2090.1471.0000.0690.000
비고0.4590.3801.0000.1920.0730.0000.2450.9900.0691.0000.134
사용여부0.0630.0470.1270.0910.0810.0640.1060.0000.0000.1341.000

Missing values

2024-01-29T00:23:04.527114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T00:23:04.943377image/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.
2024-01-29T00:23:05.079287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

노드 관리번호(ID)노드명노드구분코드권역코드 행자부 시군구 권역코드노드유형코드회전제한유무비고X좌표Y좌표접근로개수적용 시작일적용 시작 시간적용 종료일적용 종료 시간사용여부검지범위
46211690000900호국교육원입구11691030<NA>154251.1725464488.2852<NA><NA><NA><NA><NA>130
144481170001500생성노드111700<NA>191422.7931437646.799622017-09-214:30:07<NA><NA>140
142472350001200생성노드123500<NA>197024.4521468068.324322013-01-114:30:19<NA><NA>120
8237210000089아인스월드2210104<NA><NA>177391.3571445816.8392<NA>2012-01-170:00:00<NA><NA>130
52091660007600없음11661030<NA>177144.3044443931.2621<NA><NA><NA><NA><NA>130
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