Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells70558
Missing cells (%)54.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory121.0 B

Variable types

Numeric9
Categorical2
Text2

Dataset

Description당진시 공간정보활용시스템 도엽레이어 정보(500도엽,1000도엽, 2500도엽, 5000도엽 등)에 대한 데이터로 도엽시퀀스, 도형지형지물부호, 도엽번호 등의 항목을 제공합니다.
Author충청남도 당진시
URLhttps://www.data.go.kr/data/15091575/fileData.do

Alerts

500도엽지형지물부호 has constant value ""Constant
500도엽시퀀스 is highly overall correlated with 500도엽번호시퀀스 and 6 other fieldsHigh correlation
500도엽번호시퀀스 is highly overall correlated with 500도엽시퀀스 and 6 other fieldsHigh correlation
1000도엽시퀀스 is highly overall correlated with 500도엽시퀀스 and 6 other fieldsHigh correlation
1000도엽지형지물부호 is highly overall correlated with 2500도엽지형지물부호High correlation
1000도엽번호 is highly overall correlated with 2500도엽시퀀스 and 2 other fieldsHigh correlation
2500도엽시퀀스 is highly overall correlated with 500도엽시퀀스 and 7 other fieldsHigh correlation
2500도엽번호시퀀스 is highly overall correlated with 500도엽시퀀스 and 7 other fieldsHigh correlation
5000도엽시퀀스 is highly overall correlated with 500도엽시퀀스 and 6 other fieldsHigh correlation
5000도엽번호 is highly overall correlated with 500도엽시퀀스 and 6 other fieldsHigh correlation
2500도엽지형지물부호 is highly overall correlated with 500도엽시퀀스 and 8 other fieldsHigh correlation
2500도엽지형지물부호 is highly imbalanced (70.3%)Imbalance
1000도엽시퀀스 has 7461 (74.6%) missing valuesMissing
1000도엽지형지물부호 has 7462 (74.6%) missing valuesMissing
1000도엽번호 has 7461 (74.6%) missing valuesMissing
2500도엽시퀀스 has 9474 (94.7%) missing valuesMissing
2500도엽번호 has 9474 (94.7%) missing valuesMissing
2500도엽번호시퀀스 has 9474 (94.7%) missing valuesMissing
5000도엽시퀀스 has 9876 (98.8%) missing valuesMissing
5000도엽번호 has 9876 (98.8%) missing valuesMissing
500도엽시퀀스 has unique valuesUnique
500도엽번호 has unique valuesUnique
500도엽번호시퀀스 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:57:37.059635
Analysis finished2023-12-12 05:57:47.980721
Duration10.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

500도엽시퀀스
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6139.6794
Minimum1
Maximum12296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:57:48.054514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile589.95
Q13069.5
median6147.5
Q39220.25
95-th percentile11672.05
Maximum12296
Range12295
Interquartile range (IQR)6150.75

Descriptive statistics

Standard deviation3554.6836
Coefficient of variation (CV)0.57896892
Kurtosis-1.2005858
Mean6139.6794
Median Absolute Deviation (MAD)3075.5
Skewness-0.0055807886
Sum61396794
Variance12635775
MonotonicityNot monotonic
2023-12-12T14:57:48.191124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1642 1
 
< 0.1%
12160 1
 
< 0.1%
7788 1
 
< 0.1%
10526 1
 
< 0.1%
7581 1
 
< 0.1%
3317 1
 
< 0.1%
5921 1
 
< 0.1%
8792 1
 
< 0.1%
9213 1
 
< 0.1%
5116 1
 
< 0.1%
Other values (9990) 9990
99.9%
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%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
12296 1
< 0.1%
12295 1
< 0.1%
12294 1
< 0.1%
12293 1
< 0.1%
12292 1
< 0.1%
12291 1
< 0.1%
12290 1
< 0.1%
12289 1
< 0.1%
12288 1
< 0.1%
12287 1
< 0.1%

500도엽지형지물부호
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
ZD100
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZD100
2nd rowZD100
3rd rowZD100
4th rowZD100
5th rowZD100

Common Values

ValueCountFrequency (%)
ZD100 10000
100.0%

Length

2023-12-12T14:57:48.322531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:57:48.436671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
zd100 10000
100.0%

500도엽번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T14:57:48.655346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100000
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row366041760D
2nd row366030348C
3rd row366031326C
4th row376142527A
5th row366030654B
ValueCountFrequency (%)
366041760d 1
 
< 0.1%
366030592b 1
 
< 0.1%
366030100b 1
 
< 0.1%
366031735c 1
 
< 0.1%
366030742b 1
 
< 0.1%
366030325c 1
 
< 0.1%
366030738c 1
 
< 0.1%
366031905b 1
 
< 0.1%
366031218a 1
 
< 0.1%
376142416b 1
 
< 0.1%
Other values (9990) 9990
99.9%
2023-12-12T14:57:49.074876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 22144
22.1%
3 19036
19.0%
0 15658
15.7%
1 8984
9.0%
4 5671
 
5.7%
2 5588
 
5.6%
7 3834
 
3.8%
5 3729
 
3.7%
8 2693
 
2.7%
9 2663
 
2.7%
Other values (4) 10000
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 90000
90.0%
Uppercase Letter 10000
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 22144
24.6%
3 19036
21.2%
0 15658
17.4%
1 8984
10.0%
4 5671
 
6.3%
2 5588
 
6.2%
7 3834
 
4.3%
5 3729
 
4.1%
8 2693
 
3.0%
9 2663
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
D 2549
25.5%
A 2525
25.2%
C 2469
24.7%
B 2457
24.6%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
90.0%
Latin 10000
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 22144
24.6%
3 19036
21.2%
0 15658
17.4%
1 8984
10.0%
4 5671
 
6.3%
2 5588
 
6.2%
7 3834
 
4.3%
5 3729
 
4.1%
8 2693
 
3.0%
9 2663
 
3.0%
Latin
ValueCountFrequency (%)
D 2549
25.5%
A 2525
25.2%
C 2469
24.7%
B 2457
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 22144
22.1%
3 19036
19.0%
0 15658
15.7%
1 8984
9.0%
4 5671
 
5.7%
2 5588
 
5.6%
7 3834
 
3.8%
5 3729
 
3.7%
8 2693
 
2.7%
9 2663
 
2.7%
Other values (4) 10000
10.0%

500도엽번호시퀀스
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6138.6794
Minimum0
Maximum12295
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:57:49.268777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile588.95
Q13068.5
median6146.5
Q39219.25
95-th percentile11671.05
Maximum12295
Range12295
Interquartile range (IQR)6150.75

Descriptive statistics

Standard deviation3554.6836
Coefficient of variation (CV)0.57906324
Kurtosis-1.2005858
Mean6138.6794
Median Absolute Deviation (MAD)3075.5
Skewness-0.0055807886
Sum61386794
Variance12635775
MonotonicityNot monotonic
2023-12-12T14:57:49.444121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1641 1
 
< 0.1%
12159 1
 
< 0.1%
7787 1
 
< 0.1%
10525 1
 
< 0.1%
7580 1
 
< 0.1%
3316 1
 
< 0.1%
5920 1
 
< 0.1%
8791 1
 
< 0.1%
9212 1
 
< 0.1%
5115 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
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%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
12295 1
< 0.1%
12294 1
< 0.1%
12293 1
< 0.1%
12292 1
< 0.1%
12291 1
< 0.1%
12290 1
< 0.1%
12289 1
< 0.1%
12288 1
< 0.1%
12287 1
< 0.1%
12286 1
< 0.1%

1000도엽시퀀스
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2539
Distinct (%)100.0%
Missing7461
Missing (%)74.6%
Infinite0
Infinite (%)0.0%
Mean1537.2666
Minimum1
Maximum3119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:57:49.615215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile155.9
Q1744
median1522
Q32316.5
95-th percentile2952.2
Maximum3119
Range3118
Interquartile range (IQR)1572.5

Descriptive statistics

Standard deviation903.11266
Coefficient of variation (CV)0.58747951
Kurtosis-1.2145071
Mean1537.2666
Median Absolute Deviation (MAD)785
Skewness0.038644994
Sum3903120
Variance815612.47
MonotonicityNot monotonic
2023-12-12T14:57:49.782625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1275 1
 
< 0.1%
2169 1
 
< 0.1%
1499 1
 
< 0.1%
876 1
 
< 0.1%
579 1
 
< 0.1%
2072 1
 
< 0.1%
1155 1
 
< 0.1%
1637 1
 
< 0.1%
182 1
 
< 0.1%
287 1
 
< 0.1%
Other values (2529) 2529
 
25.3%
(Missing) 7461
74.6%
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%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
3119 1
< 0.1%
3117 1
< 0.1%
3116 1
< 0.1%
3115 1
< 0.1%
3114 1
< 0.1%
3113 1
< 0.1%
3112 1
< 0.1%
3110 1
< 0.1%
3109 1
< 0.1%
3108 1
< 0.1%

1000도엽지형지물부호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)3.9%
Missing7462
Missing (%)74.6%
Infinite0
Infinite (%)0.0%
Mean48.352246
Minimum0
Maximum99
Zeros27
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:57:49.964542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q124
median48
Q373
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)49

Descriptive statistics

Standard deviation28.697827
Coefficient of variation (CV)0.5935159
Kurtosis-1.1823882
Mean48.352246
Median Absolute Deviation (MAD)25
Skewness0.056888545
Sum122718
Variance823.56527
MonotonicityNot monotonic
2023-12-12T14:57:50.145421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 33
 
0.3%
50 30
 
0.3%
26 30
 
0.3%
19 30
 
0.3%
96 30
 
0.3%
8 30
 
0.3%
6 30
 
0.3%
49 29
 
0.3%
77 29
 
0.3%
9 29
 
0.3%
Other values (90) 2238
 
22.4%
(Missing) 7462
74.6%
ValueCountFrequency (%)
0 27
0.3%
1 26
0.3%
2 22
0.2%
3 24
0.2%
4 25
0.2%
5 29
0.3%
6 30
0.3%
7 25
0.2%
8 30
0.3%
9 29
0.3%
ValueCountFrequency (%)
99 21
0.2%
98 24
0.2%
97 24
0.2%
96 30
0.3%
95 25
0.2%
94 22
0.2%
93 25
0.2%
92 19
0.2%
91 24
0.2%
90 24
0.2%

1000도엽번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2539
Distinct (%)100.0%
Missing7461
Missing (%)74.6%
Infinite0
Infinite (%)0.0%
Mean3.6706931 × 108
Minimum3.660205 × 108
Maximum3.761525 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:57:50.313282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.660205 × 108
5-th percentile3.6603012 × 108
Q13.6603078 × 108
median3.6603157 × 108
Q33.6604119 × 108
95-th percentile3.7615213 × 108
Maximum3.761525 × 108
Range10131999
Interquartile range (IQR)10414.5

Descriptive statistics

Standard deviation3067177.5
Coefficient of variation (CV)0.0083558539
Kurtosis4.8914246
Mean3.6706931 × 108
Median Absolute Deviation (MAD)1125
Skewness2.6244119
Sum9.3198898 × 1011
Variance9.4075779 × 1012
MonotonicityNot monotonic
2023-12-12T14:57:50.784472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
376152273 1
 
< 0.1%
366030356 1
 
< 0.1%
366030730 1
 
< 0.1%
366042224 1
 
< 0.1%
366021068 1
 
< 0.1%
366030218 1
 
< 0.1%
366031524 1
 
< 0.1%
366030497 1
 
< 0.1%
366040648 1
 
< 0.1%
366041736 1
 
< 0.1%
Other values (2529) 2529
 
25.3%
(Missing) 7461
74.6%
ValueCountFrequency (%)
366020500 1
< 0.1%
366020504 1
< 0.1%
366020505 1
< 0.1%
366020506 1
< 0.1%
366020508 1
< 0.1%
366020509 1
< 0.1%
366020510 1
< 0.1%
366020514 1
< 0.1%
366020515 1
< 0.1%
366020516 1
< 0.1%
ValueCountFrequency (%)
376152499 1
< 0.1%
376152400 1
< 0.1%
376152398 1
< 0.1%
376152397 1
< 0.1%
376152396 1
< 0.1%
376152395 1
< 0.1%
376152394 1
< 0.1%
376152392 1
< 0.1%
376152391 1
< 0.1%
376152385 1
< 0.1%

2500도엽시퀀스
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct526
Distinct (%)100.0%
Missing9474
Missing (%)94.7%
Infinite0
Infinite (%)0.0%
Mean314.78707
Minimum1
Maximum619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:57:50.971184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33.25
Q1160.25
median315
Q3470.75
95-th percentile588.75
Maximum619
Range618
Interquartile range (IQR)310.5

Descriptive statistics

Standard deviation179.10117
Coefficient of variation (CV)0.56895975
Kurtosis-1.2011169
Mean314.78707
Median Absolute Deviation (MAD)155.5
Skewness-0.029318539
Sum165578
Variance32077.231
MonotonicityNot monotonic
2023-12-12T14:57:51.168990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170 1
 
< 0.1%
52 1
 
< 0.1%
497 1
 
< 0.1%
368 1
 
< 0.1%
534 1
 
< 0.1%
278 1
 
< 0.1%
517 1
 
< 0.1%
106 1
 
< 0.1%
525 1
 
< 0.1%
285 1
 
< 0.1%
Other values (516) 516
 
5.2%
(Missing) 9474
94.7%
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%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
619 1
< 0.1%
618 1
< 0.1%
617 1
< 0.1%
616 1
< 0.1%
615 1
< 0.1%
614 1
< 0.1%
613 1
< 0.1%
611 1
< 0.1%
610 1
< 0.1%
609 1
< 0.1%

2500도엽지형지물부호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9474 
ZD300
 
526

Length

Max length5
Median length4
Mean length4.0526
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9474
94.7%
ZD300 526
 
5.3%

Length

2023-12-12T14:57:51.352585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:57:51.463428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9474
94.7%
zd300 526
 
5.3%

2500도엽번호
Text

MISSING 

Distinct526
Distinct (%)100.0%
Missing9474
Missing (%)94.7%
Memory size156.2 KiB
2023-12-12T14:57:51.830972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4208
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique526 ?
Unique (%)100.0%

Sample

1st row3660415J
2nd row3660310B
3rd row3660325H
4th row3660323A
5th row3660316A
ValueCountFrequency (%)
3660431f 1
 
0.2%
3660332f 1
 
0.2%
3660229h 1
 
0.2%
3660324d 1
 
0.2%
3660220j 1
 
0.2%
3660414d 1
 
0.2%
3660311h 1
 
0.2%
3660342i 1
 
0.2%
3660311d 1
 
0.2%
3660416c 1
 
0.2%
Other values (516) 516
98.1%
2023-12-12T14:57:52.376465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 1036
24.6%
3 1006
23.9%
0 507
12.0%
4 349
 
8.3%
1 283
 
6.7%
2 181
 
4.3%
7 123
 
2.9%
5 89
 
2.1%
D 59
 
1.4%
B 57
 
1.4%
Other values (10) 518
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3682
87.5%
Uppercase Letter 526
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 1036
28.1%
3 1006
27.3%
0 507
13.8%
4 349
 
9.5%
1 283
 
7.7%
2 181
 
4.9%
7 123
 
3.3%
5 89
 
2.4%
9 55
 
1.5%
8 53
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
D 59
11.2%
B 57
10.8%
J 55
10.5%
C 55
10.5%
I 54
10.3%
G 52
9.9%
A 50
9.5%
H 48
9.1%
F 48
9.1%
E 48
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3682
87.5%
Latin 526
 
12.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6 1036
28.1%
3 1006
27.3%
0 507
13.8%
4 349
 
9.5%
1 283
 
7.7%
2 181
 
4.9%
7 123
 
3.3%
5 89
 
2.4%
9 55
 
1.5%
8 53
 
1.4%
Latin
ValueCountFrequency (%)
D 59
11.2%
B 57
10.8%
J 55
10.5%
C 55
10.5%
I 54
10.3%
G 52
9.9%
A 50
9.5%
H 48
9.1%
F 48
9.1%
E 48
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 1036
24.6%
3 1006
23.9%
0 507
12.0%
4 349
 
8.3%
1 283
 
6.7%
2 181
 
4.3%
7 123
 
2.9%
5 89
 
2.1%
D 59
 
1.4%
B 57
 
1.4%
Other values (10) 518
12.3%

2500도엽번호시퀀스
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct526
Distinct (%)100.0%
Missing9474
Missing (%)94.7%
Infinite0
Infinite (%)0.0%
Mean313.78707
Minimum0
Maximum618
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:57:52.556146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.25
Q1159.25
median314
Q3469.75
95-th percentile587.75
Maximum618
Range618
Interquartile range (IQR)310.5

Descriptive statistics

Standard deviation179.10117
Coefficient of variation (CV)0.57077296
Kurtosis-1.2011169
Mean313.78707
Median Absolute Deviation (MAD)155.5
Skewness-0.029318539
Sum165052
Variance32077.231
MonotonicityNot monotonic
2023-12-12T14:57:52.701239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169 1
 
< 0.1%
51 1
 
< 0.1%
496 1
 
< 0.1%
367 1
 
< 0.1%
533 1
 
< 0.1%
277 1
 
< 0.1%
516 1
 
< 0.1%
105 1
 
< 0.1%
524 1
 
< 0.1%
284 1
 
< 0.1%
Other values (516) 516
 
5.2%
(Missing) 9474
94.7%
ValueCountFrequency (%)
0 1
< 0.1%
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%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
618 1
< 0.1%
617 1
< 0.1%
616 1
< 0.1%
615 1
< 0.1%
614 1
< 0.1%
613 1
< 0.1%
612 1
< 0.1%
610 1
< 0.1%
609 1
< 0.1%
608 1
< 0.1%

5000도엽시퀀스
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct124
Distinct (%)100.0%
Missing9876
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean76.483871
Minimum1
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:57:52.858510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.15
Q137.75
median78.5
Q3115.25
95-th percentile144.85
Maximum152
Range151
Interquartile range (IQR)77.5

Descriptive statistics

Standard deviation44.401253
Coefficient of variation (CV)0.58053094
Kurtosis-1.1873033
Mean76.483871
Median Absolute Deviation (MAD)38.5
Skewness-0.003608816
Sum9484
Variance1971.4713
MonotonicityNot monotonic
2023-12-12T14:57:53.037469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106 1
 
< 0.1%
126 1
 
< 0.1%
75 1
 
< 0.1%
121 1
 
< 0.1%
135 1
 
< 0.1%
139 1
 
< 0.1%
138 1
 
< 0.1%
70 1
 
< 0.1%
53 1
 
< 0.1%
18 1
 
< 0.1%
Other values (114) 114
 
1.1%
(Missing) 9876
98.8%
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%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
152 1
< 0.1%
151 1
< 0.1%
150 1
< 0.1%
148 1
< 0.1%
147 1
< 0.1%
146 1
< 0.1%
145 1
< 0.1%
144 1
< 0.1%
143 1
< 0.1%
142 1
< 0.1%

5000도엽번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct124
Distinct (%)100.0%
Missing9876
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean36758231
Minimum36602010
Maximum37615095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:57:53.208366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36602010
5-th percentile36603001
Q136603030
median36603068
Q336604062
95-th percentile37615080
Maximum37615095
Range1013085
Interquartile range (IQR)1032.5

Descriptive statistics

Standard deviation365748.89
Coefficient of variation (CV)0.0099501224
Kurtosis1.8281369
Mean36758231
Median Absolute Deviation (MAD)54
Skewness1.9490723
Sum4.5580206 × 109
Variance1.3377225 × 1011
MonotonicityNot monotonic
2023-12-12T14:57:53.389552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36604092 1
 
< 0.1%
36604012 1
 
< 0.1%
36603049 1
 
< 0.1%
36604062 1
 
< 0.1%
37614087 1
 
< 0.1%
37614079 1
 
< 0.1%
37614099 1
 
< 0.1%
36603034 1
 
< 0.1%
36603075 1
 
< 0.1%
36602040 1
 
< 0.1%
Other values (114) 114
 
1.1%
(Missing) 9876
98.8%
ValueCountFrequency (%)
36602010 1
< 0.1%
36602030 1
< 0.1%
36602040 1
< 0.1%
36602049 1
< 0.1%
36602050 1
< 0.1%
36602060 1
< 0.1%
36603001 1
< 0.1%
36603002 1
< 0.1%
36603003 1
< 0.1%
36603004 1
< 0.1%
ValueCountFrequency (%)
37615095 1
< 0.1%
37615094 1
< 0.1%
37615093 1
< 0.1%
37615092 1
< 0.1%
37615091 1
< 0.1%
37615083 1
< 0.1%
37615081 1
< 0.1%
37615072 1
< 0.1%
37615071 1
< 0.1%
37614100 1
< 0.1%

Interactions

2023-12-12T14:57:46.692234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:37.967322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:39.311430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:40.371309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:41.335236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:42.250929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:43.321269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:44.335697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:45.629558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:46.797790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:38.092248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:39.426426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:40.515001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:41.440560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:42.379456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:43.447181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:44.453540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:45.741207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:46.883947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:38.222211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:39.552089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:40.625981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:41.541292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:42.511461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:43.567361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:44.559580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:45.869053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:46.975513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:38.611386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:39.696611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:40.731743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:41.639190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:42.617239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:43.680162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:44.666899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:45.965610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:47.060922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:38.718185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:39.844637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:40.841718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:41.741684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:42.734049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:43.795663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:44.768276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:46.056333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:47.146548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:38.857834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:39.966682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:40.966530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:41.847795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:42.864438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:43.897216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:44.882950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:46.155939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:47.236837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:38.974369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:40.077426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:41.067771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:41.951270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:42.975623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:44.019096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:44.996138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:46.306234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:47.318732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:39.086076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:40.175987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:41.161990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:42.051217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:43.085831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:44.148983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:45.105470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:46.454383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:47.398486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:39.210431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:40.268942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:41.247188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:42.148957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:43.204634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:44.247200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:45.210430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:57:46.580458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:57:53.492168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
500도엽시퀀스500도엽번호시퀀스1000도엽시퀀스1000도엽지형지물부호1000도엽번호2500도엽시퀀스2500도엽번호시퀀스5000도엽시퀀스5000도엽번호
500도엽시퀀스1.0001.0000.9690.1850.142NaNNaNNaNNaN
500도엽번호시퀀스1.0001.0000.9690.1850.142NaNNaNNaNNaN
1000도엽시퀀스0.9690.9691.0000.5650.7201.0001.000NaNNaN
1000도엽지형지물부호0.1850.1850.5651.0000.1550.7660.7660.9630.986
1000도엽번호0.1420.1420.7200.1551.0000.7770.777NaNNaN
2500도엽시퀀스NaNNaN1.0000.7660.7771.0001.0000.9550.622
2500도엽번호시퀀스NaNNaN1.0000.7660.7771.0001.0000.9550.622
5000도엽시퀀스NaNNaNNaN0.963NaN0.9550.9551.0000.986
5000도엽번호NaNNaNNaN0.986NaN0.6220.6220.9861.000
2023-12-12T14:57:53.697022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
500도엽시퀀스500도엽번호시퀀스1000도엽시퀀스1000도엽지형지물부호1000도엽번호2500도엽시퀀스2500도엽번호시퀀스5000도엽시퀀스5000도엽번호2500도엽지형지물부호
500도엽시퀀스1.0001.0001.0000.011-0.2321.0001.0001.0000.6631.000
500도엽번호시퀀스1.0001.0001.0000.011-0.2321.0001.0001.0000.6631.000
1000도엽시퀀스1.0001.0001.0000.011-0.2321.0001.0001.0000.6631.000
1000도엽지형지물부호0.0110.0110.0111.0000.0760.1430.1430.0520.1651.000
1000도엽번호-0.232-0.232-0.2320.0761.000-0.530-0.5300.0470.0301.000
2500도엽시퀀스1.0001.0001.0000.143-0.5301.0001.0001.0000.6631.000
2500도엽번호시퀀스1.0001.0001.0000.143-0.5301.0001.0001.0000.6631.000
5000도엽시퀀스1.0001.0001.0000.0520.0471.0001.0001.0000.6631.000
5000도엽번호0.6630.6630.6630.1650.0300.6630.6630.6631.0001.000
2500도엽지형지물부호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T14:57:47.537435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:57:47.708083image/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.
2023-12-12T14:57:47.874534image/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

500도엽시퀀스500도엽지형지물부호500도엽번호500도엽번호시퀀스1000도엽시퀀스1000도엽지형지물부호1000도엽번호2500도엽시퀀스2500도엽지형지물부호2500도엽번호2500도엽번호시퀀스5000도엽시퀀스5000도엽번호
16411642ZD100366041760D1641164292366030592<NA><NA><NA><NA><NA><NA>
1003210033ZD100366030348C10032<NA><NA><NA><NA><NA><NA><NA><NA><NA>
50595060ZD100366031326C5059<NA><NA><NA><NA><NA><NA><NA><NA><NA>
1196811969ZD100376142527A11968<NA><NA><NA><NA><NA><NA><NA><NA><NA>
72397240ZD100366030654B7239<NA><NA><NA><NA><NA><NA><NA><NA><NA>
1004910050ZD100366030241C10049<NA><NA><NA><NA><NA><NA><NA><NA><NA>
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