Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells41833
Missing cells (%)26.1%
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.
URLhttps://www.data.go.kr/data/15117308/fileData.do

Alerts

비고 is highly overall correlated with 노드유형코드 and 1 other fieldsHigh correlation
노드구분코드 is highly overall correlated with 노드 관리번호(ID) and 3 other fieldsHigh correlation
노드 관리번호(ID) is highly overall correlated with 권역코드 행자부 시군구 권역코드 and 2 other fieldsHigh correlation
권역코드 행자부 시군구 권역코드 is highly overall correlated with 노드 관리번호(ID)High 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 (83.2%)Imbalance
사용여부 is highly imbalanced (71.0%)Imbalance
노드명 has 686 (6.9%) missing valuesMissing
접근로개수 has 8067 (80.7%) missing valuesMissing
적용 시작일 has 7042 (70.4%) missing valuesMissing
적용 시작 시간 has 7042 (70.4%) missing valuesMissing
적용 종료일 has 9498 (95.0%) missing valuesMissing
적용 종료 시간 has 9498 (95.0%) missing valuesMissing
노드 관리번호(ID) has unique valuesUnique
노드유형코드 has 906 (9.1%) zerosZeros
접근로개수 has 118 (1.2%) zerosZeros

Reproduction

Analysis started2023-12-12 21:53:57.335353
Analysis finished2023-12-12 21:54:06.443057
Duration9.11 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%
Mean7.7814599 × 108
Minimum99999999
Maximum2.3500019 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:54:06.843846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99999999
5-th percentile1.19 × 108
Q11.6500098 × 108
median1.7000002 × 108
Q31.6400477 × 109
95-th percentile2.1800175 × 109
Maximum2.3500019 × 109
Range2.2500019 × 109
Interquartile range (IQR)1.4750467 × 109

Descriptive statistics

Standard deviation7.8046465 × 108
Coefficient of variation (CV)1.0029797
Kurtosis-1.3357629
Mean7.7814599 × 108
Median Absolute Deviation (MAD)49000001
Skewness0.63401393
Sum7.7814599 × 1012
Variance6.0912507 × 1017
MonotonicityNot monotonic
2023-12-13T06:54:07.001211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650013900 1
 
< 0.1%
119000003 1
 
< 0.1%
166000848 1
 
< 0.1%
1670028200 1
 
< 0.1%
167000069 1
 
< 0.1%
1680072100 1
 
< 0.1%
2240014100 1
 
< 0.1%
168000620 1
 
< 0.1%
1670027200 1
 
< 0.1%
210000120 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
99999999 1
< 0.1%
100000001 1
< 0.1%
100000007 1
< 0.1%
100000008 1
< 0.1%
100000009 1
< 0.1%
100000011 1
< 0.1%
100000012 1
< 0.1%
100000014 1
< 0.1%
100000015 1
< 0.1%
100000017 1
< 0.1%
ValueCountFrequency (%)
2350001900 1
< 0.1%
2350001800 1
< 0.1%
2350001600 1
< 0.1%
2350001500 1
< 0.1%
2350001200 1
< 0.1%
2350001100 1
< 0.1%
2350001000 1
< 0.1%
2350000800 1
< 0.1%
2350000600 1
< 0.1%
2350000500 1
< 0.1%

노드명
Text

MISSING 

Distinct4942
Distinct (%)53.1%
Missing686
Missing (%)6.9%
Memory size156.2 KiB
2023-12-13T06:54:07.352119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length5.6414
Min length1

Characters and Unicode

Total characters52544
Distinct characters665
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3497 ?
Unique (%)37.5%

Sample

1st row없음
2nd row옥구5교
3rd row대동아파트
4th row계산주공
5th row생성노드
ValueCountFrequency (%)
생성노드 1588
 
16.9%
없음 706
 
7.5%
101
 
1.1%
대동아파트 18
 
0.2%
금호아파트 15
 
0.2%
풍림아파트 11
 
0.1%
신동아아파트 10
 
0.1%
현대아파트 10
 
0.1%
제물포역 9
 
0.1%
계산역 8
 
0.1%
Other values (4957) 6906
73.6%
2023-12-13T06:54:07.843940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
성 1827
 
3.5%
드 1693
 
3.2%
노 1632
 
3.1%
생 1615
 
3.1%
아 1046
 
2.0%
리 1045
 
2.0%
교 961
 
1.8%
트 954
 
1.8%
동 929
 
1.8%
파 866
 
1.6%
Other values (655) 39976
76.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48938
93.1%
Decimal Number 1192
 
2.3%
Uppercase Letter 664
 
1.3%
Open Punctuation 573
 
1.1%
Close Punctuation 571
 
1.1%
Other Punctuation 384
 
0.7%
Dash Punctuation 115
 
0.2%
Space Separator 68
 
0.1%
Lowercase Letter 37
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 1827
 
3.7%
드 1693
 
3.5%
노 1632
 
3.3%
생 1615
 
3.3%
아 1046
 
2.1%
리 1045
 
2.1%
교 961
 
2.0%
트 954
 
1.9%
동 929
 
1.9%
파 866
 
1.8%
Other values (604) 36370
74.3%
Uppercase Letter
ValueCountFrequency (%)
C 186
28.0%
I 132
19.9%
S 54
 
8.1%
J 52
 
7.8%
K 46
 
6.9%
T 40
 
6.0%
G 36
 
5.4%
L 22
 
3.3%
B 17
 
2.6%
H 16
 
2.4%
Other values (12) 63
 
9.5%
Decimal Number
ValueCountFrequency (%)
1 334
28.0%
2 257
21.6%
3 157
13.2%
4 87
 
7.3%
0 86
 
7.2%
6 82
 
6.9%
5 82
 
6.9%
8 40
 
3.4%
7 39
 
3.3%
9 28
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
e 17
45.9%
t 7
18.9%
s 4
 
10.8%
d 3
 
8.1%
n 1
 
2.7%
i 1
 
2.7%
f 1
 
2.7%
m 1
 
2.7%
y 1
 
2.7%
g 1
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 374
97.4%
/ 9
 
2.3%
· 1
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 573
100.0%
Close Punctuation
ValueCountFrequency (%)
) 571
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 115
100.0%
Space Separator
ValueCountFrequency (%)
68
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48939
93.1%
Common 2904
 
5.5%
Latin 701
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 1827
 
3.7%
드 1693
 
3.5%
노 1632
 
3.3%
생 1615
 
3.3%
아 1046
 
2.1%
리 1045
 
2.1%
교 961
 
2.0%
트 954
 
1.9%
동 929
 
1.9%
파 866
 
1.8%
Other values (605) 36371
74.3%
Latin
ValueCountFrequency (%)
C 186
26.5%
I 132
18.8%
S 54
 
7.7%
J 52
 
7.4%
K 46
 
6.6%
T 40
 
5.7%
G 36
 
5.1%
L 22
 
3.1%
e 17
 
2.4%
B 17
 
2.4%
Other values (22) 99
14.1%
Common
ValueCountFrequency (%)
( 573
19.7%
) 571
19.7%
. 374
12.9%
1 334
11.5%
2 257
8.8%
3 157
 
5.4%
- 115
 
4.0%
4 87
 
3.0%
0 86
 
3.0%
6 82
 
2.8%
Other values (8) 268
9.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48938
93.1%
ASCII 3604
 
6.9%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
성 1827
 
3.7%
드 1693
 
3.5%
노 1632
 
3.3%
생 1615
 
3.3%
아 1046
 
2.1%
리 1045
 
2.1%
교 961
 
2.0%
트 954
 
1.9%
동 929
 
1.9%
파 866
 
1.8%
Other values (604) 36370
74.3%
ASCII
ValueCountFrequency (%)
( 573
15.9%
) 571
15.8%
. 374
10.4%
1 334
9.3%
2 257
 
7.1%
C 186
 
5.2%
3 157
 
4.4%
I 132
 
3.7%
- 115
 
3.2%
4 87
 
2.4%
Other values (39) 818
22.7%
None
ValueCountFrequency (%)
㈜ 1
50.0%
· 1
50.0%

노드구분코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
6036 
1
3964 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 6036
60.4%
1 3964
39.6%

Length

2023-12-13T06:54:07.994685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:08.095866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 6036
60.4%
1 3964
39.6%

권역코드 행자부 시군구 권역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.8028
Minimum100
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:54:08.223049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile114
Q1163
median166
Q3169
95-th percentile224
Maximum999
Range899
Interquartile range (IQR)6

Descriptive statistics

Standard deviation31.55895
Coefficient of variation (CV)0.18585647
Kurtosis47.761129
Mean169.8028
Median Absolute Deviation (MAD)3
Skewness1.9803681
Sum1698028
Variance995.96731
MonotonicityNot monotonic
2023-12-13T06:54:08.382406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
168 1253
12.5%
169 1050
 
10.5%
165 905
 
9.0%
161 888
 
8.9%
166 726
 
7.3%
164 657
 
6.6%
167 527
 
5.3%
163 506
 
5.1%
232 364
 
3.6%
210 333
 
3.3%
Other values (51) 2791
27.9%
ValueCountFrequency (%)
100 62
0.6%
101 36
0.4%
102 60
0.6%
103 23
 
0.2%
104 11
 
0.1%
105 21
 
0.2%
106 17
 
0.2%
107 26
0.3%
108 6
 
0.1%
109 11
 
0.1%
ValueCountFrequency (%)
999 1
 
< 0.1%
277 26
 
0.3%
235 17
 
0.2%
233 1
 
< 0.1%
232 364
3.6%
229 49
 
0.5%
226 12
 
0.1%
225 25
 
0.2%
224 317
3.2%
220 73
 
0.7%

노드유형코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.7385
Minimum0
Maximum108
Zeros906
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:54:08.503600image/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 deviation29.599022
Coefficient of variation (CV)0.31576164
Kurtosis6.128527
Mean93.7385
Median Absolute Deviation (MAD)0
Skewness-2.8493717
Sum937385
Variance876.10213
MonotonicityNot monotonic
2023-12-13T06:54:08.626654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
103 6081
60.8%
104 2135
 
21.3%
0 906
 
9.1%
101 791
 
7.9%
102 37
 
0.4%
108 29
 
0.3%
105 21
 
0.2%
ValueCountFrequency (%)
0 906
 
9.1%
101 791
 
7.9%
102 37
 
0.4%
103 6081
60.8%
104 2135
 
21.3%
105 21
 
0.2%
108 29
 
0.3%
ValueCountFrequency (%)
108 29
 
0.3%
105 21
 
0.2%
104 2135
 
21.3%
103 6081
60.8%
102 37
 
0.4%
101 791
 
7.9%
0 906
 
9.1%

회전제한유무
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
7750 
<NA>
2080 
1
 
170

Length

Max length4
Median length1
Mean length1.624
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 7750
77.5%
<NA> 2080
 
20.8%
1 170
 
1.7%

Length

2023-12-13T06:54:08.786088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:08.904164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7750
77.5%
na 2080
 
20.8%
1 170
 
1.7%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8740 
보완
 
748
0
 
225
1
 
149
신규
 
38
Other values (22)
 
100

Length

Max length23
Median length4
Mean length3.7447
Min length1

Unique

Unique9 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8740
87.4%
보완 748
 
7.5%
0 225
 
2.2%
1 149
 
1.5%
신규 38
 
0.4%
시스템노드 21
 
0.2%
구축외 20
 
0.2%
2 10
 
0.1%
교차로 형태변경 7
 
0.1%
sa_tg_add 6
 
0.1%
Other values (17) 36
 
0.4%

Length

2023-12-13T06:54:09.028440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 8740
87.2%
보완 748
 
7.5%
0 225
 
2.2%
1 149
 
1.5%
신규 38
 
0.4%
시스템노드 21
 
0.2%
구축외 20
 
0.2%
2 10
 
0.1%
교차로 9
 
0.1%
형태변경 7
 
0.1%
Other values (26) 60
 
0.6%

X좌표
Real number (ℝ)

Distinct9902
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173123.42
Minimum131132.13
Maximum215339.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:54:09.165423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum131132.13
5-th percentile148676.7
Q1167184.24
median173070.28
Q3179029.55
95-th percentile199771.86
Maximum215339.39
Range84207.258
Interquartile range (IQR)11845.31

Descriptive statistics

Standard deviation14146.732
Coefficient of variation (CV)0.081714721
Kurtosis0.26496103
Mean173123.42
Median Absolute Deviation (MAD)5909.3086
Skewness0.12693859
Sum1.7312342 × 109
Variance2.0013003 × 108
MonotonicityNot monotonic
2023-12-13T06:54:09.297252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180799.0966 2
 
< 0.1%
179598.4723 2
 
< 0.1%
196138.5766 2
 
< 0.1%
166745.0457 2
 
< 0.1%
189309.7754 2
 
< 0.1%
182104.1398 2
 
< 0.1%
182433.9938 2
 
< 0.1%
174569.7172 2
 
< 0.1%
182842.6519 2
 
< 0.1%
197440.5334 2
 
< 0.1%
Other values (9892) 9980
99.8%
ValueCountFrequency (%)
131132.1292 1
< 0.1%
131267.6408 1
< 0.1%
131791.1137 1
< 0.1%
131802.9246 1
< 0.1%
132247.7218 1
< 0.1%
132302.435 1
< 0.1%
132447.9843 1
< 0.1%
132453.5692 1
< 0.1%
132582.5208 1
< 0.1%
132606.7237 1
< 0.1%
ValueCountFrequency (%)
215339.3868 1
< 0.1%
215235.1185 1
< 0.1%
213782.2863 1
< 0.1%
213155.5191 1
< 0.1%
213071.9 1
< 0.1%
212761.7653 1
< 0.1%
212351.2349 1
< 0.1%
212175.5995 1
< 0.1%
212161.3024 1
< 0.1%
212059.6457 1
< 0.1%

Y좌표
Real number (ℝ)

Distinct9899
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446198.64
Minimum412566.93
Maximum484503.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:54:09.417196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum412566.93
5-th percentile431000.12
Q1438607.21
median444614.2
Q3451865.19
95-th percentile470154.03
Maximum484503.05
Range71936.123
Interquartile range (IQR)13257.989

Descriptive statistics

Standard deviation11792.907
Coefficient of variation (CV)0.026429724
Kurtosis0.58395584
Mean446198.64
Median Absolute Deviation (MAD)6427.0467
Skewness0.54185394
Sum4.4619864 × 109
Variance1.3907265 × 108
MonotonicityNot monotonic
2023-12-13T06:54:09.550237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
437672.3201 2
 
< 0.1%
461508.6567 2
 
< 0.1%
445462.8965 2
 
< 0.1%
432758.2383 2
 
< 0.1%
461753.6444 2
 
< 0.1%
444712.7498 2
 
< 0.1%
438256.6374 2
 
< 0.1%
447099.1525 2
 
< 0.1%
421042.9391 2
 
< 0.1%
431764.469 2
 
< 0.1%
Other values (9889) 9980
99.8%
ValueCountFrequency (%)
412566.9304 1
< 0.1%
412611.7672 1
< 0.1%
413025.7883 1
< 0.1%
413736.3934 1
< 0.1%
413833.952 1
< 0.1%
413859.8791 1
< 0.1%
413868.2322 1
< 0.1%
414377.1818 2
< 0.1%
414625.6023 1
< 0.1%
414666.8946 1
< 0.1%
ValueCountFrequency (%)
484503.0533 1
< 0.1%
484446.4068 1
< 0.1%
484430.773 1
< 0.1%
484300.6279 1
< 0.1%
484290.415 1
< 0.1%
484289.128 1
< 0.1%
484275.2167 1
< 0.1%
484269.2167 1
< 0.1%
484221.2159 1
< 0.1%
483828.4068 1
< 0.1%

접근로개수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)0.6%
Missing8067
Missing (%)80.7%
Infinite0
Infinite (%)0.0%
Mean2.2612519
Minimum0
Maximum16
Zeros118
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:54:09.655474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q32
95-th percentile5
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3780796
Coefficient of variation (CV)0.60943214
Kurtosis10.647197
Mean2.2612519
Median Absolute Deviation (MAD)0
Skewness2.3058397
Sum4371
Variance1.8991034
MonotonicityNot monotonic
2023-12-13T06:54:09.757656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 1226
 
12.3%
1 181
 
1.8%
4 154
 
1.5%
3 153
 
1.5%
0 118
 
1.2%
6 31
 
0.3%
5 31
 
0.3%
8 31
 
0.3%
7 4
 
< 0.1%
10 3
 
< 0.1%
(Missing) 8067
80.7%
ValueCountFrequency (%)
0 118
 
1.2%
1 181
 
1.8%
2 1226
12.3%
3 153
 
1.5%
4 154
 
1.5%
5 31
 
0.3%
6 31
 
0.3%
7 4
 
< 0.1%
8 31
 
0.3%
10 3
 
< 0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
10 3
 
< 0.1%
8 31
 
0.3%
7 4
 
< 0.1%
6 31
 
0.3%
5 31
 
0.3%
4 154
 
1.5%
3 153
 
1.5%
2 1226
12.3%
1 181
 
1.8%

적용 시작일
Date

MISSING 

Distinct470
Distinct (%)15.9%
Missing7042
Missing (%)70.4%
Memory size156.2 KiB
Minimum2004-11-17 00:00:00
Maximum2020-05-30 00:00:00
2023-12-13T06:54:09.875237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:09.999984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

적용 시작 시간
Date

MISSING 

Distinct293
Distinct (%)9.9%
Missing7042
Missing (%)70.4%
Memory size156.2 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 22:02:28
2023-12-13T06:54:10.115873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:10.239475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

적용 종료일
Text

MISSING 

Distinct161
Distinct (%)32.1%
Missing9498
Missing (%)95.0%
Memory size156.2 KiB
2023-12-13T06:54:10.519333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5020
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

Unique91 ?
Unique (%)18.1%

Sample

1st row2007-10-06
2nd row2014-09-18
3rd row2007-10-06
4th row2020-12-29
5th row2007-10-06
ValueCountFrequency (%)
2007-10-06 178
35.5%
2014-10-14 11
 
2.2%
2010-06-17 9
 
1.8%
2013-07-17 9
 
1.8%
2016-06-24 8
 
1.6%
2011-06-23 8
 
1.6%
2020-12-29 7
 
1.4%
2018-01-26 7
 
1.4%
2014-07-04 6
 
1.2%
2007-11-13 6
 
1.2%
Other values (151) 253
50.4%
2023-12-13T06:54:10.909034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1527
30.4%
- 1004
20.0%
2 760
15.1%
1 723
14.4%
6 292
 
5.8%
7 274
 
5.5%
3 131
 
2.6%
4 118
 
2.4%
9 79
 
1.6%
8 77
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4016
80.0%
Dash Punctuation 1004
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1527
38.0%
2 760
18.9%
1 723
18.0%
6 292
 
7.3%
7 274
 
6.8%
3 131
 
3.3%
4 118
 
2.9%
9 79
 
2.0%
8 77
 
1.9%
5 35
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 1004
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5020
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1527
30.4%
- 1004
20.0%
2 760
15.1%
1 723
14.4%
6 292
 
5.8%
7 274
 
5.5%
3 131
 
2.6%
4 118
 
2.4%
9 79
 
1.6%
8 77
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1527
30.4%
- 1004
20.0%
2 760
15.1%
1 723
14.4%
6 292
 
5.8%
7 274
 
5.5%
3 131
 
2.6%
4 118
 
2.4%
9 79
 
1.6%
8 77
 
1.5%

적용 종료 시간
Date

MISSING 

Distinct95
Distinct (%)18.9%
Missing9498
Missing (%)95.0%
Memory size156.2 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 22:27:00
2023-12-13T06:54:11.062335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:11.214474image/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
9491 
0
 
509

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 9491
94.9%
0 509
 
5.1%

Length

2023-12-13T06:54:11.419293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:11.518714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9491
94.9%
0 509
 
5.1%

검지범위
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.0154
Minimum5
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:54:11.620598image/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 deviation5.6469772
Coefficient of variation (CV)0.19462
Kurtosis24.757425
Mean29.0154
Median Absolute Deviation (MAD)0
Skewness1.9241676
Sum290154
Variance31.888352
MonotonicityNot monotonic
2023-12-13T06:54:11.754939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
30 8389
83.9%
20 1115
 
11.2%
40 207
 
2.1%
10 192
 
1.9%
50 35
 
0.4%
80 31
 
0.3%
60 26
 
0.3%
5 2
 
< 0.1%
70 2
 
< 0.1%
44 1
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
10 192
 
1.9%
20 1115
 
11.2%
30 8389
83.9%
40 207
 
2.1%
44 1
 
< 0.1%
50 35
 
0.4%
60 26
 
0.3%
70 2
 
< 0.1%
80 31
 
0.3%
ValueCountFrequency (%)
80 31
 
0.3%
70 2
 
< 0.1%
60 26
 
0.3%
50 35
 
0.4%
44 1
 
< 0.1%
40 207
 
2.1%
30 8389
83.9%
20 1115
 
11.2%
10 192
 
1.9%
5 2
 
< 0.1%

Interactions

2023-12-13T06:54:04.950972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:53:59.985419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:00.829211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:01.638009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:02.441384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:03.188688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:04.041394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:05.043777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:00.101391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:00.959349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:01.756992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:02.541560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:03.304068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:04.171012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:05.151585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:00.206886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:01.063458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:01.865395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:02.641585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:03.415137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:04.300373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:05.263387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:00.350928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:01.173414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:01.958901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:02.736701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:03.543358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:04.416984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:05.394186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:00.438814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:01.264510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:02.046324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:02.827016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:03.651608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:04.539526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:05.526599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:00.551006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:01.380881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:02.170524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:02.950033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:03.782809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:04.676956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:05.646665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:00.703898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:01.519597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:02.309157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:03.065672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:03.913915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:04.818184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:54:11.877991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드 관리번호(ID)노드구분코드권역코드 행자부 시군구 권역코드노드유형코드회전제한유무비고X좌표Y좌표접근로개수적용 종료 시간사용여부검지범위
노드 관리번호(ID)1.0001.0000.8680.6190.4590.7630.5900.4090.3770.9790.0910.666
노드구분코드1.0001.0000.0630.5740.2281.0000.1880.097NaN0.9880.0220.665
권역코드 행자부 시군구 권역코드0.8680.0631.0000.0730.1470.4890.4380.3610.2140.7780.0220.256
노드유형코드0.6190.5740.0731.0000.079NaN0.3170.1960.5650.9560.1640.901
회전제한유무0.4590.2280.1470.0791.0000.0000.1900.1630.1490.5900.0000.273
비고0.7631.0000.489NaN0.0001.0000.4400.2070.0000.1280.1450.523
X좌표0.5900.1880.4380.3170.1900.4401.0000.6660.3220.9190.1090.313
Y좌표0.4090.0970.3610.1960.1630.2070.6661.0000.2290.7990.1050.156
접근로개수0.377NaN0.2140.5650.1490.0000.3220.2291.0000.0000.0330.970
적용 종료 시간0.9790.9880.7780.9560.5900.1280.9190.7990.0001.0000.0000.849
사용여부0.0910.0220.0220.1640.0000.1450.1090.1050.0330.0001.0000.122
검지범위0.6660.6650.2560.9010.2730.5230.3130.1560.9700.8490.1221.000
2023-12-13T06:54:12.077220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비고사용여부회전제한유무노드구분코드
비고1.0000.1140.0000.990
사용여부0.1141.0000.0000.014
회전제한유무0.0000.0001.0000.147
노드구분코드0.9900.0140.1471.000
2023-12-13T06:54:12.181914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드 관리번호(ID)권역코드 행자부 시군구 권역코드노드유형코드X좌표Y좌표접근로개수검지범위노드구분코드회전제한유무비고사용여부
노드 관리번호(ID)1.0000.503-0.5100.0220.071-0.128-0.3601.0000.3450.4500.068
권역코드 행자부 시군구 권역코드0.5031.000-0.024-0.1020.210-0.122-0.1430.1040.2430.3860.037
노드유형코드-0.510-0.0241.0000.034-0.0010.2390.4680.3890.0511.0000.105
X좌표0.022-0.1020.0341.000-0.1130.214-0.0710.1440.1460.1900.083
Y좌표0.0710.210-0.001-0.1131.000-0.001-0.0180.0750.1250.0780.081
접근로개수-0.128-0.1220.2390.214-0.0011.0000.6451.0000.1070.0000.033
검지범위-0.360-0.1430.468-0.071-0.0180.6451.0000.5060.2050.2360.091
노드구분코드1.0000.1040.3890.1440.0751.0000.5061.0000.1470.9900.014
회전제한유무0.3450.2430.0510.1460.1250.1070.2050.1471.0000.0000.000
비고0.4500.3861.0000.1900.0780.0000.2360.9900.0001.0000.114
사용여부0.0680.0370.1050.0830.0810.0330.0910.0140.0000.1141.000

Missing values

2023-12-13T06:54:05.839813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:54:06.098698image/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.
2023-12-13T06:54:06.322049image/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
33811650013900없음11651030<NA>176816.2969437527.4341<NA><NA><NA><NA><NA>130
39292240019200옥구5교12241010보완174515.1471426790.04062<NA><NA><NA><NA>120
1291165000256대동아파트21651030<NA>176564.0414438015.8238<NA><NA><NA><NA><NA>130
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