Overview

Dataset statistics

Number of variables17
Number of observations1513
Missing cells1450
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory223.2 KiB
Average record size in memory151.1 B

Variable types

Numeric11
Categorical4
Text2

Dataset

Description경상남도 도로대장전산화 시스템 데이터의 중장기개방계획에 따른 데이터입니다. 시스템 상에서의 각 도로별 시설물 기본정보를 가지고 있으며, 도로대장의 옹벽 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15091932

Alerts

식별번호 is highly overall correlated with 관리번호 and 2 other fieldsHigh correlation
관리번호 is highly overall correlated with 식별번호 and 2 other fieldsHigh correlation
노선번호 is highly overall correlated with 식별번호 and 2 other fieldsHigh correlation
위치_시점 is highly overall correlated with 위치_종점High correlation
위치_종점 is highly overall correlated with 위치_시점High correlation
도로종류 is highly overall correlated with 식별번호 and 2 other fieldsHigh correlation
관리기관 is highly imbalanced (82.3%)Imbalance
도로종류 is highly imbalanced (55.5%)Imbalance
이력코드 is highly imbalanced (96.3%)Imbalance
사진 has 298 (19.7%) missing valuesMissing
비고 has 1145 (75.7%) missing valuesMissing
식별번호 has unique valuesUnique
관리번호 has 68 (4.5%) zerosZeros
높이_최소 has 143 (9.5%) zerosZeros
has 304 (20.1%) zerosZeros

Reproduction

Analysis started2023-12-11 00:30:48.114904
Analysis finished2023-12-11 00:31:02.885068
Duration14.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

식별번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1513
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean757
Minimum1
Maximum1513
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-11T09:31:02.977181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile76.6
Q1379
median757
Q31135
95-th percentile1437.4
Maximum1513
Range1512
Interquartile range (IQR)756

Descriptive statistics

Standard deviation436.90979
Coefficient of variation (CV)0.57715957
Kurtosis-1.2
Mean757
Median Absolute Deviation (MAD)378
Skewness0
Sum1145341
Variance190890.17
MonotonicityStrictly increasing
2023-12-11T09:31:03.474699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1006 1
 
0.1%
1015 1
 
0.1%
1014 1
 
0.1%
1013 1
 
0.1%
1012 1
 
0.1%
1011 1
 
0.1%
1010 1
 
0.1%
1009 1
 
0.1%
1008 1
 
0.1%
Other values (1503) 1503
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1513 1
0.1%
1512 1
0.1%
1511 1
0.1%
1510 1
0.1%
1509 1
0.1%
1508 1
0.1%
1507 1
0.1%
1506 1
0.1%
1505 1
0.1%
1504 1
0.1%

관리번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1436
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8321671.2
Minimum0
Maximum10990030
Zeros68
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-11T09:31:03.663415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile300008.6
Q110020012
median10180002
Q310340041
95-th percentile10890002
Maximum10990030
Range10990030
Interquartile range (IQR)320029

Descriptive statistics

Standard deviation3964776.2
Coefficient of variation (CV)0.4764399
Kurtosis0.22722638
Mean8321671.2
Median Absolute Deviation (MAD)160029
Skewness-1.4808695
Sum1.2590688 × 1010
Variance1.571945 × 1013
MonotonicityNot monotonic
2023-12-11T09:31:03.813551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68
 
4.5%
690001 2
 
0.1%
690002 2
 
0.1%
10180077 2
 
0.1%
10090007 2
 
0.1%
10400002 2
 
0.1%
600001 2
 
0.1%
10770077 2
 
0.1%
10400003 2
 
0.1%
690003 2
 
0.1%
Other values (1426) 1427
94.3%
ValueCountFrequency (%)
0 68
4.5%
300001 1
 
0.1%
300002 1
 
0.1%
300003 1
 
0.1%
300004 1
 
0.1%
300005 1
 
0.1%
300006 1
 
0.1%
300007 1
 
0.1%
300008 1
 
0.1%
300009 1
 
0.1%
ValueCountFrequency (%)
10990030 1
0.1%
10990029 1
0.1%
10990028 1
0.1%
10990027 1
0.1%
10990026 1
0.1%
10990025 1
0.1%
10990024 1
0.1%
10990023 1
0.1%
10990022 1
0.1%
10990021 1
0.1%

관리기관
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
1683
1447 
1684
 
59
1682
 
7

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1683
2nd row1683
3rd row1683
4th row1683
5th row1683

Common Values

ValueCountFrequency (%)
1683 1447
95.6%
1684 59
 
3.9%
1682 7
 
0.5%

Length

2023-12-11T09:31:03.973141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:31:04.101319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1683 1447
95.6%
1684 59
 
3.9%
1682 7
 
0.5%

도로종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
1504
1373 
1507
140 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1504
2nd row1504
3rd row1504
4th row1504
5th row1504

Common Values

ValueCountFrequency (%)
1504 1373
90.7%
1507 140
 
9.3%

Length

2023-12-11T09:31:04.204871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:31:04.337990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1504 1373
90.7%
1507 140
 
9.3%

노선번호
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean877.33113
Minimum30
Maximum1099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-11T09:31:04.460838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile60
Q11003
median1018
Q31037
95-th percentile1089
Maximum1099
Range1069
Interquartile range (IQR)34

Descriptive statistics

Standard deviation356.71175
Coefficient of variation (CV)0.40658736
Kurtosis1.4865397
Mean877.33113
Median Absolute Deviation (MAD)16
Skewness-1.8543278
Sum1327402
Variance127243.27
MonotonicityNot monotonic
2023-12-11T09:31:04.593147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1018 119
 
7.9%
60 97
 
6.4%
1077 83
 
5.5%
1024 68
 
4.5%
1002 64
 
4.2%
1001 59
 
3.9%
1022 56
 
3.7%
1034 55
 
3.6%
1084 53
 
3.5%
1010 53
 
3.5%
Other values (32) 806
53.3%
ValueCountFrequency (%)
30 11
 
0.7%
37 46
3.0%
58 14
 
0.9%
60 97
6.4%
67 32
 
2.1%
69 39
2.6%
907 5
 
0.3%
1001 59
3.9%
1002 64
4.2%
1003 31
 
2.0%
ValueCountFrequency (%)
1099 30
 
2.0%
1089 48
3.2%
1084 53
3.5%
1080 31
 
2.0%
1077 83
5.5%
1051 13
 
0.9%
1049 20
 
1.3%
1047 25
 
1.7%
1042 17
 
1.1%
1041 25
 
1.7%

구간번호
Real number (ℝ)

Distinct17
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1625909
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-11T09:31:04.712686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile12
Maximum19
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4943267
Coefficient of variation (CV)0.67685525
Kurtosis-0.082981952
Mean5.1625909
Median Absolute Deviation (MAD)2
Skewness0.7962538
Sum7811
Variance12.210319
MonotonicityNot monotonic
2023-12-11T09:31:04.859056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 228
15.1%
2 193
12.8%
3 175
11.6%
5 170
11.2%
4 162
10.7%
7 140
9.3%
6 105
6.9%
8 69
 
4.6%
11 68
 
4.5%
10 57
 
3.8%
Other values (7) 146
9.6%
ValueCountFrequency (%)
1 228
15.1%
2 193
12.8%
3 175
11.6%
4 162
10.7%
5 170
11.2%
6 105
6.9%
7 140
9.3%
8 69
 
4.6%
9 55
 
3.6%
10 57
 
3.8%
ValueCountFrequency (%)
19 1
 
0.1%
16 2
 
0.1%
15 9
 
0.6%
14 16
 
1.1%
13 33
2.2%
12 30
2.0%
11 68
4.5%
10 57
3.8%
9 55
3.6%
8 69
4.6%

이력코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
0
1507 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1507
99.6%
1 6
 
0.4%

Length

2023-12-11T09:31:04.992779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:31:05.096700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1507
99.6%
1 6
 
0.4%

위치_시점
Real number (ℝ)

HIGH CORRELATION 

Distinct1390
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6274997
Minimum0
Maximum28.692
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-11T09:31:05.203178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2806
Q11.99
median5.019
Q38.416
95-th percentile13.2088
Maximum28.692
Range28.692
Interquartile range (IQR)6.426

Descriptive statistics

Standard deviation4.2607585
Coefficient of variation (CV)0.75713172
Kurtosis1.6078352
Mean5.6274997
Median Absolute Deviation (MAD)3.153
Skewness0.94463324
Sum8514.407
Variance18.154063
MonotonicityNot monotonic
2023-12-11T09:31:05.385380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
0.4%
7.5 3
 
0.2%
0.76 3
 
0.2%
0.678 3
 
0.2%
4.62 3
 
0.2%
6.62 3
 
0.2%
10.98 3
 
0.2%
3.043 3
 
0.2%
11.67 2
 
0.1%
4.104 2
 
0.1%
Other values (1380) 1482
98.0%
ValueCountFrequency (%)
0.0 6
0.4%
0.001 1
 
0.1%
0.002 1
 
0.1%
0.005 2
 
0.1%
0.006 1
 
0.1%
0.008 1
 
0.1%
0.011 1
 
0.1%
0.012 2
 
0.1%
0.015 1
 
0.1%
0.019 2
 
0.1%
ValueCountFrequency (%)
28.692 1
0.1%
27.86 1
0.1%
27.785 1
0.1%
27.192 1
0.1%
24.8 1
0.1%
23.312 1
0.1%
19.898 1
0.1%
18.873 1
0.1%
17.914 1
0.1%
17.767 1
0.1%

위치_종점
Real number (ℝ)

HIGH CORRELATION 

Distinct1397
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7190337
Minimum0
Maximum57.815
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-11T09:31:05.547400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3426
Q12.071
median5.102
Q38.489
95-th percentile13.3494
Maximum57.815
Range57.815
Interquartile range (IQR)6.418

Descriptive statistics

Standard deviation4.4298384
Coefficient of variation (CV)0.77457812
Kurtosis13.203453
Mean5.7190337
Median Absolute Deviation (MAD)3.165
Skewness1.8211907
Sum8652.898
Variance19.623468
MonotonicityNot monotonic
2023-12-11T09:31:05.689628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.76 3
 
0.2%
6.635 3
 
0.2%
3.625 3
 
0.2%
0.047 3
 
0.2%
9.603 2
 
0.1%
4.461 2
 
0.1%
5.41 2
 
0.1%
5.528 2
 
0.1%
3.59 2
 
0.1%
5.36 2
 
0.1%
Other values (1387) 1489
98.4%
ValueCountFrequency (%)
0.0 1
0.1%
0.002 1
0.1%
0.02 1
0.1%
0.022 1
0.1%
0.025 1
0.1%
0.026 1
0.1%
0.035 1
0.1%
0.036 1
0.1%
0.038 2
0.1%
0.045 1
0.1%
ValueCountFrequency (%)
57.815 1
0.1%
28.725 1
0.1%
27.885 1
0.1%
27.225 1
0.1%
24.815 1
0.1%
23.34 1
0.1%
19.957 1
0.1%
18.98 1
0.1%
18.0 1
0.1%
17.798 1
0.1%

위치_방향
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
1
760 
0
753 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 760
50.2%
0 753
49.8%

Length

2023-12-11T09:31:05.827378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:31:05.955103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 760
50.2%
0 753
49.8%

연장
Real number (ℝ)

Distinct258
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.669227
Minimum0
Maximum1315
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-11T09:31:06.080215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q127
median47
Q390
95-th percentile209
Maximum1315
Range1315
Interquartile range (IQR)63

Descriptive statistics

Standard deviation79.490204
Coefficient of variation (CV)1.1248206
Kurtosis48.815155
Mean70.669227
Median Absolute Deviation (MAD)26
Skewness4.8853028
Sum106922.54
Variance6318.6926
MonotonicityNot monotonic
2023-12-11T09:31:06.271125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.0 51
 
3.4%
30.0 44
 
2.9%
40.0 34
 
2.2%
10.0 28
 
1.9%
15.0 27
 
1.8%
18.0 27
 
1.8%
22.0 24
 
1.6%
35.0 24
 
1.6%
29.0 24
 
1.6%
50.0 23
 
1.5%
Other values (248) 1207
79.8%
ValueCountFrequency (%)
0.0 1
 
0.1%
2.0 2
 
0.1%
4.0 4
 
0.3%
5.0 2
 
0.1%
6.0 11
 
0.7%
7.0 4
 
0.3%
8.0 11
 
0.7%
9.0 8
 
0.5%
10.0 28
1.9%
11.0 11
 
0.7%
ValueCountFrequency (%)
1315.0 1
0.1%
666.0 1
0.1%
623.0 1
0.1%
622.0 1
0.1%
566.0 1
0.1%
515.0 1
0.1%
506.0 1
0.1%
499.0 1
0.1%
460.0 1
0.1%
441.0 1
0.1%

종류
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing7
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean4324.917
Minimum4300
Maximum4399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-11T09:31:06.424841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4300
5-th percentile4301
Q14301
median4302
Q34309
95-th percentile4399
Maximum4399
Range99
Interquartile range (IQR)8

Descriptive statistics

Standard deviation40.914826
Coefficient of variation (CV)0.009460257
Kurtosis-0.4136844
Mean4324.917
Median Absolute Deviation (MAD)1
Skewness1.2536218
Sum6513325
Variance1674.023
MonotonicityNot monotonic
2023-12-11T09:31:06.567945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4301 572
37.8%
4399 351
23.2%
4302 330
21.8%
4308 142
 
9.4%
4300 56
 
3.7%
4309 35
 
2.3%
4304 13
 
0.9%
4306 6
 
0.4%
4305 1
 
0.1%
(Missing) 7
 
0.5%
ValueCountFrequency (%)
4300 56
 
3.7%
4301 572
37.8%
4302 330
21.8%
4304 13
 
0.9%
4305 1
 
0.1%
4306 6
 
0.4%
4308 142
 
9.4%
4309 35
 
2.3%
4399 351
23.2%
ValueCountFrequency (%)
4399 351
23.2%
4309 35
 
2.3%
4308 142
 
9.4%
4306 6
 
0.4%
4305 1
 
0.1%
4304 13
 
0.9%
4302 330
21.8%
4301 572
37.8%
4300 56
 
3.7%

높이_최대
Real number (ℝ)

Distinct76
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5559815
Minimum0
Maximum20.5
Zeros14
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-11T09:31:06.724716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8
Q11.2
median2
Q33
95-th percentile6
Maximum20.5
Range20.5
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.0898807
Coefficient of variation (CV)0.81764314
Kurtosis19.120821
Mean2.5559815
Median Absolute Deviation (MAD)1
Skewness3.4090569
Sum3867.2
Variance4.3676014
MonotonicityNot monotonic
2023-12-11T09:31:06.882093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0 227
15.0%
1.0 209
 
13.8%
3.0 159
 
10.5%
2.5 84
 
5.6%
1.5 71
 
4.7%
4.0 58
 
3.8%
5.0 48
 
3.2%
6.0 46
 
3.0%
1.8 44
 
2.9%
1.2 43
 
2.8%
Other values (66) 524
34.6%
ValueCountFrequency (%)
0.0 14
 
0.9%
0.2 1
 
0.1%
0.3 6
 
0.4%
0.4 4
 
0.3%
0.5 16
 
1.1%
0.6 10
 
0.7%
0.7 11
 
0.7%
0.8 19
 
1.3%
0.9 21
 
1.4%
1.0 209
13.8%
ValueCountFrequency (%)
20.5 1
0.1%
20.0 2
0.1%
19.0 1
0.1%
18.0 1
0.1%
17.0 1
0.1%
16.0 1
0.1%
15.0 1
0.1%
13.5 1
0.1%
12.6 1
0.1%
12.0 2
0.1%

높이_최소
Real number (ℝ)

ZEROS 

Distinct58
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6271183
Minimum0
Maximum20
Zeros143
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-11T09:31:07.041575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4.3
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6563185
Coefficient of variation (CV)1.017946
Kurtosis27.204323
Mean1.6271183
Median Absolute Deviation (MAD)0.6
Skewness3.8260154
Sum2461.83
Variance2.743391
MonotonicityNot monotonic
2023-12-11T09:31:07.257786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 392
25.9%
0.0 143
 
9.5%
2.0 132
 
8.7%
3.0 88
 
5.8%
0.5 76
 
5.0%
1.5 71
 
4.7%
2.5 41
 
2.7%
0.2 39
 
2.6%
1.2 39
 
2.6%
0.3 38
 
2.5%
Other values (48) 454
30.0%
ValueCountFrequency (%)
0.0 143
9.5%
0.1 2
 
0.1%
0.2 39
 
2.6%
0.3 38
 
2.5%
0.4 9
 
0.6%
0.5 76
5.0%
0.6 17
 
1.1%
0.7 15
 
1.0%
0.8 25
 
1.7%
0.9 13
 
0.9%
ValueCountFrequency (%)
20.0 1
 
0.1%
18.0 1
 
0.1%
17.0 1
 
0.1%
16.0 1
 
0.1%
9.0 9
0.6%
8.0 6
0.4%
7.5 3
 
0.2%
7.2 1
 
0.1%
7.0 3
 
0.2%
6.5 1
 
0.1%


Real number (ℝ)

ZEROS 

Distinct34
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59074686
Minimum0
Maximum27
Zeros304
Zeros (%)20.1%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-11T09:31:07.419067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median0.5
Q30.7
95-th percentile1
Maximum27
Range27
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation1.0756429
Coefficient of variation (CV)1.8208186
Kurtosis287.75321
Mean0.59074686
Median Absolute Deviation (MAD)0.2
Skewness14.207131
Sum893.8
Variance1.1570076
MonotonicityNot monotonic
2023-12-11T09:31:07.560476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.5 411
27.2%
0.0 304
20.1%
0.7 273
18.0%
1.0 178
11.8%
0.3 121
 
8.0%
0.25 71
 
4.7%
0.8 51
 
3.4%
0.4 18
 
1.2%
2.0 16
 
1.1%
0.2 11
 
0.7%
Other values (24) 59
 
3.9%
ValueCountFrequency (%)
0.0 304
20.1%
0.1 2
 
0.1%
0.15 11
 
0.7%
0.2 11
 
0.7%
0.25 71
 
4.7%
0.3 121
 
8.0%
0.4 18
 
1.2%
0.5 411
27.2%
0.6 6
 
0.4%
0.7 273
18.0%
ValueCountFrequency (%)
27.0 1
 
0.1%
16.6 1
 
0.1%
11.0 1
 
0.1%
10.8 1
 
0.1%
7.0 5
0.3%
6.0 1
 
0.1%
5.2 1
 
0.1%
5.0 3
0.2%
4.5 1
 
0.1%
3.4 1
 
0.1%

사진
Text

MISSING 

Distinct1212
Distinct (%)99.8%
Missing298
Missing (%)19.7%
Memory size11.9 KiB
2023-12-11T09:31:07.812948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.986008
Min length12

Characters and Unicode

Total characters15778
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1209 ?
Unique (%)99.5%

Sample

1st row100309F04959U
2nd row100309F02666U
3rd row100309F00233U
4th row100308F01615U
5th row100307F10275D
ValueCountFrequency (%)
102202f01966d 2
 
0.2%
100902f01118u 2
 
0.2%
101809f01667d 2
 
0.2%
104201f02175d 1
 
0.1%
103701f08172u 1
 
0.1%
103701f04708d 1
 
0.1%
103701f04798d 1
 
0.1%
103701f06001d 1
 
0.1%
103701f06114d 1
 
0.1%
103701f07240d 1
 
0.1%
Other values (1202) 1202
98.9%
2023-12-11T09:31:08.188952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4826
30.6%
1 2407
15.3%
F 1210
 
7.7%
2 857
 
5.4%
4 847
 
5.4%
7 841
 
5.3%
3 803
 
5.1%
6 727
 
4.6%
8 695
 
4.4%
5 689
 
4.4%
Other values (5) 1876
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
84.6%
Uppercase Letter 2428
 
15.4%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4826
36.2%
1 2407
18.0%
2 857
 
6.4%
4 847
 
6.3%
7 841
 
6.3%
3 803
 
6.0%
6 727
 
5.4%
8 695
 
5.2%
5 689
 
5.2%
9 656
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
F 1210
49.8%
D 614
25.3%
U 599
24.7%
L 5
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
u 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
84.6%
Latin 2430
 
15.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4826
36.2%
1 2407
18.0%
2 857
 
6.4%
4 847
 
6.3%
7 841
 
6.3%
3 803
 
6.0%
6 727
 
5.4%
8 695
 
5.2%
5 689
 
5.2%
9 656
 
4.9%
Latin
ValueCountFrequency (%)
F 1210
49.8%
D 614
25.3%
U 599
24.7%
L 5
 
0.2%
u 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15778
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4826
30.6%
1 2407
15.3%
F 1210
 
7.7%
2 857
 
5.4%
4 847
 
5.4%
7 841
 
5.3%
3 803
 
5.1%
6 727
 
4.6%
8 695
 
4.4%
5 689
 
4.4%
Other values (5) 1876
 
11.9%

비고
Text

MISSING 

Distinct66
Distinct (%)17.9%
Missing1145
Missing (%)75.7%
Memory size11.9 KiB
2023-12-11T09:31:08.411124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length4.3288043
Min length1

Characters and Unicode

Total characters1593
Distinct characters75
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)8.2%

Sample

1st row돌망태2단
2nd row돌망태2단
3rd row돌망태3단
4th row돌망태2단
5th row돌망태2단
ValueCountFrequency (%)
개비온 62
15.9%
돌망태 39
 
10.0%
l형 34
 
8.7%
0 25
 
6.4%
역t형 25
 
6.4%
돌망태2단 20
 
5.1%
옹벽 19
 
4.9%
옹벽(개비온 18
 
4.6%
돌망태3단 17
 
4.4%
l형옹벽 10
 
2.6%
Other values (51) 120
30.8%
2023-12-11T09:31:08.771898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
106
 
6.7%
102
 
6.4%
101
 
6.3%
100
 
6.3%
98
 
6.2%
97
 
6.1%
96
 
6.0%
96
 
6.0%
91
 
5.7%
( 72
 
4.5%
Other values (65) 634
39.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1184
74.3%
Decimal Number 114
 
7.2%
Uppercase Letter 99
 
6.2%
Open Punctuation 72
 
4.5%
Close Punctuation 72
 
4.5%
Space Separator 21
 
1.3%
Other Punctuation 17
 
1.1%
Math Symbol 14
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
106
9.0%
102
8.6%
101
8.5%
100
8.4%
98
8.3%
97
8.2%
96
8.1%
96
8.1%
91
 
7.7%
62
 
5.2%
Other values (45) 235
19.8%
Decimal Number
ValueCountFrequency (%)
0 35
30.7%
2 33
28.9%
3 24
21.1%
1 10
 
8.8%
5 5
 
4.4%
6 4
 
3.5%
4 3
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
L 56
56.6%
T 33
33.3%
H 7
 
7.1%
W 1
 
1.0%
R 1
 
1.0%
M 1
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 16
94.1%
, 1
 
5.9%
Math Symbol
ValueCountFrequency (%)
= 7
50.0%
~ 7
50.0%
Open Punctuation
ValueCountFrequency (%)
( 72
100.0%
Close Punctuation
ValueCountFrequency (%)
) 72
100.0%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1184
74.3%
Common 310
 
19.5%
Latin 99
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
106
9.0%
102
8.6%
101
8.5%
100
8.4%
98
8.3%
97
8.2%
96
8.1%
96
8.1%
91
 
7.7%
62
 
5.2%
Other values (45) 235
19.8%
Common
ValueCountFrequency (%)
( 72
23.2%
) 72
23.2%
0 35
11.3%
2 33
10.6%
3 24
 
7.7%
21
 
6.8%
. 16
 
5.2%
1 10
 
3.2%
= 7
 
2.3%
~ 7
 
2.3%
Other values (4) 13
 
4.2%
Latin
ValueCountFrequency (%)
L 56
56.6%
T 33
33.3%
H 7
 
7.1%
W 1
 
1.0%
R 1
 
1.0%
M 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1184
74.3%
ASCII 409
 
25.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
106
9.0%
102
8.6%
101
8.5%
100
8.4%
98
8.3%
97
8.2%
96
8.1%
96
8.1%
91
 
7.7%
62
 
5.2%
Other values (45) 235
19.8%
ASCII
ValueCountFrequency (%)
( 72
17.6%
) 72
17.6%
L 56
13.7%
0 35
8.6%
T 33
8.1%
2 33
8.1%
3 24
 
5.9%
21
 
5.1%
. 16
 
3.9%
1 10
 
2.4%
Other values (10) 37
9.0%

Interactions

2023-12-11T09:31:01.254342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:49.728988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:51.357385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:52.519130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:53.568277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:54.633225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:55.780762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:56.819735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:58.201369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:59.202771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:00.099047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:01.363837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:49.855450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:51.457488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:52.611314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:53.673043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:54.758390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:55.895648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:56.937616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:58.301354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:59.297606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:00.196286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:01.454516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:49.982843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:51.550154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:52.701134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:53.769879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:54.894304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:55.991817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:57.027321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:58.416914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:59.372338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:00.290243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:01.550185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:50.102909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:51.645775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:52.787884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:53.865983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:54.975888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:56.078748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:57.121871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:58.517703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:59.447534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:00.375694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:01.646844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:50.230570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:51.762857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:52.888675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:53.951323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:55.079499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:56.179029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:57.504348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:58.603810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:59.527115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:00.495567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:01.776416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:50.346248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:51.877739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:52.985679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:54.044563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:55.175759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:56.271194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:57.608317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:58.683363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:59.597627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:00.601292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:01.864678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:50.495139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:51.981125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:53.079228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:54.140321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:55.267254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:56.362200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:57.695629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:58.774109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:59.688333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:00.693912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:01.962010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:50.614570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:52.084349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:53.172858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:54.227463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:55.356777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:56.448650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:57.793067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:58.875029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:59.764030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:00.790481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:02.067518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:50.737726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:52.208817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:53.258764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:54.335822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:55.447196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:56.541750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:57.883647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:58.951280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:59.842787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:00.886513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:02.173125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:51.174942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:52.315874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:53.357671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:54.438321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:55.559436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:56.625622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:57.968514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:59.028126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:59.917055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:00.996706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:02.297457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:51.270757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:52.415342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:53.458434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:54.531236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:55.665735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:56.733969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:58.092071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:59.116016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:30:59.999339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:01.149657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:31:08.872039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향연장종류높이_최대높이_최소비고
식별번호1.0000.6950.6020.9980.7400.5710.2250.2450.2360.1010.0540.3540.2780.2050.0520.928
관리번호0.6951.0000.3300.4040.9970.1970.0000.1570.0700.0140.0000.0380.0000.1180.1130.713
관리기관0.6020.3301.0000.0350.1790.5380.0000.1630.0950.0270.0770.1710.1030.1100.0000.926
도로종류0.9980.4040.0351.0000.4810.1990.0000.0840.0740.0580.0000.0000.0000.0380.1170.636
노선번호0.7400.9970.1790.4811.0000.1680.0000.1310.0130.0280.0000.0570.0000.0830.0330.685
구간번호0.5710.1970.5380.1990.1681.0000.0870.1960.1510.0720.0000.1320.1650.0000.0000.827
이력코드0.2250.0000.0000.0000.0000.0871.0000.0000.0000.0000.0000.0690.0000.0000.000NaN
위치_시점0.2450.1570.1630.0840.1310.1960.0001.0000.9860.1510.0440.0970.0000.0270.0000.825
위치_종점0.2360.0700.0950.0740.0130.1510.0000.9861.0000.1680.0000.1030.0000.0000.0000.861
위치_방향0.1010.0140.0270.0580.0280.0720.0000.1510.1681.0000.0400.0730.0350.0700.0700.440
연장0.0540.0000.0770.0000.0000.0000.0000.0440.0000.0401.0000.0620.0000.0590.0000.771
종류0.3540.0380.1710.0000.0570.1320.0690.0970.1030.0730.0621.0000.0230.0000.0540.949
높이_최대0.2780.0000.1030.0000.0000.1650.0000.0000.0000.0350.0000.0231.0000.7660.1440.796
높이_최소0.2050.1180.1100.0380.0830.0000.0000.0270.0000.0700.0590.0000.7661.0000.0530.873
0.0520.1130.0000.1170.0330.0000.0000.0000.0000.0700.0000.0540.1440.0531.0000.000
비고0.9280.7130.9260.6360.6850.827NaN0.8250.8610.4400.7710.9490.7960.8730.0001.000
2023-12-11T09:31:09.010864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리기관위치_방향이력코드도로종류
관리기관1.0000.0440.0000.058
위치_방향0.0441.0000.0000.037
이력코드0.0000.0001.0000.000
도로종류0.0580.0370.0001.000
2023-12-11T09:31:09.121896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호노선번호구간번호위치_시점위치_종점연장종류높이_최대높이_최소관리기관도로종류이력코드위치_방향
식별번호1.0000.5020.6870.068-0.084-0.085-0.0110.1030.0290.003-0.3210.4450.9560.1720.077
관리번호0.5021.0000.850-0.105-0.041-0.0410.022-0.0180.0210.016-0.1130.1140.6360.0000.023
노선번호0.6870.8501.000-0.098-0.082-0.083-0.0150.0370.0550.038-0.1540.0550.7370.0000.046
구간번호0.068-0.105-0.0981.0000.0480.0480.0020.0070.040-0.0900.1040.3890.1610.0660.054
위치_시점-0.084-0.041-0.0820.0481.0000.9990.035-0.0170.0250.0270.0720.0720.0830.0000.149
위치_종점-0.085-0.041-0.0830.0480.9991.0000.050-0.0170.0250.0270.0720.0390.0530.0000.121
연장-0.0110.022-0.0150.0020.0350.0501.0000.0170.009-0.013-0.0210.0520.0000.0000.043
종류0.103-0.0180.0370.007-0.017-0.0170.0171.0000.175-0.0190.3380.1040.0000.0460.050
높이_최대0.0290.0210.0550.0400.0250.0250.0090.1751.0000.4610.2690.0600.0000.0000.032
높이_최소0.0030.0160.038-0.0900.0270.027-0.013-0.0190.4611.000-0.1180.0340.0440.0000.071
-0.321-0.113-0.1540.1040.0720.072-0.0210.3380.269-0.1181.0000.0000.1010.0000.057
관리기관0.4450.1140.0550.3890.0720.0390.0520.1040.0600.0340.0001.0000.0580.0000.044
도로종류0.9560.6360.7370.1610.0830.0530.0000.0000.0000.0440.1010.0581.0000.0000.037
이력코드0.1720.0000.0000.0660.0000.0000.0000.0460.0000.0000.0000.0000.0001.0000.000
위치_방향0.0770.0230.0460.0540.1490.1210.0430.0500.0320.0710.0570.0440.0370.0001.000

Missing values

2023-12-11T09:31:02.461697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:31:02.695935image/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.
2023-12-11T09:31:02.825970image/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

식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향연장종류높이_최대높이_최소사진비고
0110030032168315041003904.9595.0041.043082.01.01.0100309F04959U돌망태2단
1210030031168315041003902.6662.706040.043021.21.20.3100309F02666U<NA>
2310030030168315041003900.2330.275042.043082.01.01.0100309F00233U돌망태2단
3410030029168315041003801.6151.65035.043083.01.01.0100308F01615U돌망태3단
45100300281683150410037010.27510.335160.043082.01.01.0100307F10275D돌망태2단
56100300271683150410037010.12210.165143.043082.01.01.0100307F10122D돌망태2단
6710030026168315041003709.8699.94171.043083.01.01.0100307F09869D돌망태3단
7810030025168315041003205.2685.33162.043020.80.80.3100302F05268D<NA>
8910030024168315041003906.3156.398083.043022.02.00.3100309F06315U<NA>
91010030022168315041003905.05.048048.043082.02.00.3100309F05000U돌망태2단
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향연장종류높이_최대높이_최소사진비고
1503150401682150410021408.9038.978175.0<NA>0.00.00.0<NA><NA>
1504150501682150410021409.0489.13182.0<NA>0.00.00.0<NA><NA>
1505150601682150410021409.4229.7410319.0<NA>0.00.00.0<NA><NA>
1506150701682150410021409.3729.5611189.0<NA>0.00.00.0<NA><NA>
1507150810420020168315041042113.9934.06067.043990.00.00.0104201F03993U<NA>
1508150910420019168315041042113.9664.131164.043990.00.00.0104201F03966D<NA>
1509151010420018168315041042113.343.38040.043990.00.00.0104201F03340U<NA>
1510151110420015168315041042111.9252.01185.043093.02.00.0104201F01925D<NA>
1511151210420017168315041042112.1752.229154.043095.00.00.0104201F02175D<NA>
1512151310420016168315041042112.0912.133042.043990.00.00.0104201F02091U<NA>