Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells17501
Missing cells (%)11.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory137.0 B

Variable types

Text3
Numeric9
Boolean1
DateTime2

Dataset

Description국유림경영정보 경영계획부 이력정보 입니다.- 경영계획부번호, 이력관리번호, 차기번호, 경영계획구ID, 임반ID, 소반ID, 입목지면적,미립목지면적, 제지면적 등
Author산림청
URLhttps://www.data.go.kr/data/15120480/fileData.do

Alerts

사용여부 has constant value ""Constant
입목지면적 has 128 (1.3%) missing valuesMissing
미립목지면적 has 128 (1.3%) missing valuesMissing
제지면적 has 128 (1.3%) missing valuesMissing
소반지피에스엑스좌표 has 3857 (38.6%) missing valuesMissing
소반지피에스와이좌표 has 3857 (38.6%) missing valuesMissing
신청서번호 has 9403 (94.0%) missing valuesMissing
미립목지면적 is highly skewed (γ1 = 31.69609979)Skewed
입목지면적 has 189 (1.9%) zerosZeros
미립목지면적 has 9712 (97.1%) zerosZeros
제지면적 has 8400 (84.0%) zerosZeros

Reproduction

Analysis started2023-12-12 23:04:00.387920
Analysis finished2023-12-12 23:04:11.036256
Duration10.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct9311
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T08:04:11.200607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length20.4128
Min length19

Characters and Unicode

Total characters204128
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8686 ?
Unique (%)86.9%

Sample

1st row142_8_008-000_012-002
2nd row47_8_096-000_006-000
3rd row45_8_072-000_013-000
4th row16_7_052-000_012-004
5th row45_8_094-000_003-000
ValueCountFrequency (%)
126_2_006-000_015-001 5
 
< 0.1%
44_8_014-000_010-000 4
 
< 0.1%
98_9_065-000_002-000 4
 
< 0.1%
1001_1_001-000_005-000 4
 
< 0.1%
999_2_005-000_011-001 4
 
< 0.1%
16_8_054-000_004-000 4
 
< 0.1%
98_9_076-000_004-000 4
 
< 0.1%
143_8_035-001_024-000 4
 
< 0.1%
143_8_035-001_015-000 4
 
< 0.1%
130_8_003-000_047-000 4
 
< 0.1%
Other values (9301) 9959
99.6%
2023-12-13T08:04:11.583759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 89356
43.8%
_ 30000
 
14.7%
- 20000
 
9.8%
1 17075
 
8.4%
8 9491
 
4.6%
2 8579
 
4.2%
3 6522
 
3.2%
4 6350
 
3.1%
5 5044
 
2.5%
7 4196
 
2.1%
Other values (2) 7515
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 154128
75.5%
Connector Punctuation 30000
 
14.7%
Dash Punctuation 20000
 
9.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89356
58.0%
1 17075
 
11.1%
8 9491
 
6.2%
2 8579
 
5.6%
3 6522
 
4.2%
4 6350
 
4.1%
5 5044
 
3.3%
7 4196
 
2.7%
9 3852
 
2.5%
6 3663
 
2.4%
Connector Punctuation
ValueCountFrequency (%)
_ 30000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 204128
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 89356
43.8%
_ 30000
 
14.7%
- 20000
 
9.8%
1 17075
 
8.4%
8 9491
 
4.6%
2 8579
 
4.2%
3 6522
 
3.2%
4 6350
 
3.1%
5 5044
 
2.5%
7 4196
 
2.1%
Other values (2) 7515
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 204128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 89356
43.8%
_ 30000
 
14.7%
- 20000
 
9.8%
1 17075
 
8.4%
8 9491
 
4.6%
2 8579
 
4.2%
3 6522
 
3.2%
4 6350
 
3.1%
5 5044
 
2.5%
7 4196
 
2.1%
Other values (2) 7515
 
3.7%

이력관리번호
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6973
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:04:11.731494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum16
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2327306
Coefficient of variation (CV)0.72628915
Kurtosis18.885396
Mean1.6973
Median Absolute Deviation (MAD)0
Skewness3.4058743
Sum16973
Variance1.5196247
MonotonicityNot monotonic
2023-12-13T08:04:11.835651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 6047
60.5%
2 2419
24.2%
3 833
 
8.3%
4 381
 
3.8%
5 144
 
1.4%
6 74
 
0.7%
7 37
 
0.4%
8 26
 
0.3%
11 10
 
0.1%
9 8
 
0.1%
Other values (6) 21
 
0.2%
ValueCountFrequency (%)
1 6047
60.5%
2 2419
24.2%
3 833
 
8.3%
4 381
 
3.8%
5 144
 
1.4%
6 74
 
0.7%
7 37
 
0.4%
8 26
 
0.3%
9 8
 
0.1%
10 7
 
0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
15 1
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
12 7
 
0.1%
11 10
 
0.1%
10 7
 
0.1%
9 8
 
0.1%
8 26
0.3%
7 37
0.4%

차기번호
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2785
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:04:11.942645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median8
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9574271
Coefficient of variation (CV)0.26893276
Kurtosis4.363663
Mean7.2785
Median Absolute Deviation (MAD)0
Skewness-2.3988798
Sum72785
Variance3.8315209
MonotonicityNot monotonic
2023-12-13T08:04:12.055507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
8 6934
69.3%
7 1309
 
13.1%
9 680
 
6.8%
2 574
 
5.7%
1 390
 
3.9%
3 62
 
0.6%
6 51
 
0.5%
ValueCountFrequency (%)
1 390
 
3.9%
2 574
 
5.7%
3 62
 
0.6%
6 51
 
0.5%
7 1309
 
13.1%
8 6934
69.3%
9 680
 
6.8%
ValueCountFrequency (%)
9 680
 
6.8%
8 6934
69.3%
7 1309
 
13.1%
6 51
 
0.5%
3 62
 
0.6%
2 574
 
5.7%
1 390
 
3.9%

경영계획구아이디
Real number (ℝ)

Distinct103
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.592
Minimum0
Maximum1001
Zeros36
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:04:12.183976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q127
median51
Q3117
95-th percentile142
Maximum1001
Range1001
Interquartile range (IQR)90

Descriptive statistics

Standard deviation105.37713
Coefficient of variation (CV)1.291513
Kurtosis57.548908
Mean81.592
Median Absolute Deviation (MAD)39
Skewness6.9005626
Sum815920
Variance11104.339
MonotonicityNot monotonic
2023-12-13T08:04:12.333455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 548
 
5.5%
45 454
 
4.5%
16 406
 
4.1%
114 375
 
3.8%
111 330
 
3.3%
51 286
 
2.9%
47 280
 
2.8%
133 256
 
2.6%
116 235
 
2.4%
49 230
 
2.3%
Other values (93) 6600
66.0%
ValueCountFrequency (%)
0 36
0.4%
1 20
 
0.2%
2 31
 
0.3%
3 16
 
0.2%
5 41
0.4%
6 7
 
0.1%
7 56
0.6%
8 74
0.7%
9 81
0.8%
10 58
0.6%
ValueCountFrequency (%)
1001 14
 
0.1%
1000 24
 
0.2%
999 67
 
0.7%
147 11
 
0.1%
146 32
 
0.3%
145 90
0.9%
144 48
 
0.5%
143 199
2.0%
142 153
1.5%
141 41
 
0.4%
Distinct366
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T08:04:12.686123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70000
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

Unique47 ?
Unique (%)0.5%

Sample

1st row008-000
2nd row096-000
3rd row072-000
4th row052-000
5th row094-000
ValueCountFrequency (%)
001-000 347
 
3.5%
008-000 235
 
2.4%
004-000 233
 
2.3%
007-000 226
 
2.3%
003-000 225
 
2.2%
006-000 216
 
2.2%
002-000 212
 
2.1%
009-000 204
 
2.0%
005-000 197
 
2.0%
010-000 187
 
1.9%
Other values (356) 7718
77.2%
2023-12-13T08:04:13.188193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 41507
59.3%
- 10000
 
14.3%
1 4255
 
6.1%
2 2575
 
3.7%
3 2120
 
3.0%
4 1876
 
2.7%
5 1801
 
2.6%
6 1741
 
2.5%
7 1453
 
2.1%
9 1352
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
85.7%
Dash Punctuation 10000
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41507
69.2%
1 4255
 
7.1%
2 2575
 
4.3%
3 2120
 
3.5%
4 1876
 
3.1%
5 1801
 
3.0%
6 1741
 
2.9%
7 1453
 
2.4%
9 1352
 
2.3%
8 1320
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41507
59.3%
- 10000
 
14.3%
1 4255
 
6.1%
2 2575
 
3.7%
3 2120
 
3.0%
4 1876
 
2.7%
5 1801
 
2.6%
6 1741
 
2.5%
7 1453
 
2.1%
9 1352
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41507
59.3%
- 10000
 
14.3%
1 4255
 
6.1%
2 2575
 
3.7%
3 2120
 
3.0%
4 1876
 
2.7%
5 1801
 
2.6%
6 1741
 
2.5%
7 1453
 
2.1%
9 1352
 
1.9%
Distinct279
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T08:04:13.531898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70000
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 (%)0.9%

Sample

1st row012-002
2nd row006-000
3rd row013-000
4th row012-004
5th row003-000
ValueCountFrequency (%)
001-000 1168
 
11.7%
002-000 1050
 
10.5%
003-000 895
 
8.9%
004-000 798
 
8.0%
005-000 678
 
6.8%
006-000 548
 
5.5%
007-000 421
 
4.2%
008-000 402
 
4.0%
009-000 354
 
3.5%
010-000 273
 
2.7%
Other values (269) 3413
34.1%
2023-12-13T08:04:13.955328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 46380
66.3%
- 10000
 
14.3%
1 4324
 
6.2%
2 2428
 
3.5%
3 1665
 
2.4%
4 1357
 
1.9%
5 1104
 
1.6%
6 883
 
1.3%
7 693
 
1.0%
8 613
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
85.7%
Dash Punctuation 10000
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46380
77.3%
1 4324
 
7.2%
2 2428
 
4.0%
3 1665
 
2.8%
4 1357
 
2.3%
5 1104
 
1.8%
6 883
 
1.5%
7 693
 
1.2%
8 613
 
1.0%
9 553
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46380
66.3%
- 10000
 
14.3%
1 4324
 
6.2%
2 2428
 
3.5%
3 1665
 
2.4%
4 1357
 
1.9%
5 1104
 
1.6%
6 883
 
1.3%
7 693
 
1.0%
8 613
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46380
66.3%
- 10000
 
14.3%
1 4324
 
6.2%
2 2428
 
3.5%
3 1665
 
2.4%
4 1357
 
1.9%
5 1104
 
1.6%
6 883
 
1.3%
7 693
 
1.0%
8 613
 
0.9%

입목지면적
Real number (ℝ)

MISSING  ZEROS 

Distinct660
Distinct (%)6.7%
Missing128
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean25.195533
Minimum0
Maximum395
Zeros189
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:04:14.111320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median12
Q331
95-th percentile94
Maximum395
Range395
Interquartile range (IQR)26

Descriptive statistics

Standard deviation35.423512
Coefficient of variation (CV)1.4059442
Kurtosis14.231879
Mean25.195533
Median Absolute Deviation (MAD)9
Skewness3.1777826
Sum248730.3
Variance1254.8252
MonotonicityNot monotonic
2023-12-13T08:04:14.285014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0 572
 
5.7%
3.0 501
 
5.0%
4.0 413
 
4.1%
5.0 385
 
3.9%
6.0 371
 
3.7%
1.0 356
 
3.6%
7.0 326
 
3.3%
8.0 283
 
2.8%
10.0 271
 
2.7%
9.0 231
 
2.3%
Other values (650) 6163
61.6%
ValueCountFrequency (%)
0.0 189
1.9%
0.2 1
 
< 0.1%
0.3 1
 
< 0.1%
0.4 1
 
< 0.1%
0.5 6
 
0.1%
0.6 2
 
< 0.1%
0.7 4
 
< 0.1%
0.8 5
 
0.1%
0.9 6
 
0.1%
1.0 356
3.6%
ValueCountFrequency (%)
395.0 1
< 0.1%
380.0 1
< 0.1%
336.0 1
< 0.1%
311.0 2
< 0.1%
310.0 2
< 0.1%
290.0 1
< 0.1%
285.0 1
< 0.1%
283.8 1
< 0.1%
279.2 1
< 0.1%
275.0 1
< 0.1%

미립목지면적
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct49
Distinct (%)0.5%
Missing128
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean0.080155997
Minimum0
Maximum68
Zeros9712
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:04:14.432263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum68
Range68
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2394228
Coefficient of variation (CV)15.462633
Kurtosis1344.2118
Mean0.080155997
Median Absolute Deviation (MAD)0
Skewness31.6961
Sum791.3
Variance1.5361688
MonotonicityNot monotonic
2023-12-13T08:04:14.557341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.0 9712
97.1%
1.0 26
 
0.3%
2.0 18
 
0.2%
3.0 10
 
0.1%
4.0 8
 
0.1%
5.0 8
 
0.1%
0.5 7
 
0.1%
0.1 7
 
0.1%
6.0 6
 
0.1%
9.0 4
 
< 0.1%
Other values (39) 66
 
0.7%
(Missing) 128
 
1.3%
ValueCountFrequency (%)
0.0 9712
97.1%
0.1 7
 
0.1%
0.2 4
 
< 0.1%
0.3 3
 
< 0.1%
0.4 2
 
< 0.1%
0.5 7
 
0.1%
0.6 3
 
< 0.1%
0.7 1
 
< 0.1%
0.8 1
 
< 0.1%
0.9 2
 
< 0.1%
ValueCountFrequency (%)
68.0 1
 
< 0.1%
50.0 1
 
< 0.1%
35.0 1
 
< 0.1%
30.0 1
 
< 0.1%
22.0 1
 
< 0.1%
21.0 3
< 0.1%
20.0 3
< 0.1%
18.2 1
 
< 0.1%
14.0 1
 
< 0.1%
12.0 2
< 0.1%

제지면적
Real number (ℝ)

MISSING  ZEROS 

Distinct96
Distinct (%)1.0%
Missing128
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean0.6235312
Minimum0
Maximum132
Zeros8400
Zeros (%)84.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:04:14.691482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum132
Range132
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.7973502
Coefficient of variation (CV)6.0900725
Kurtosis359.00977
Mean0.6235312
Median Absolute Deviation (MAD)0
Skewness16.081698
Sum6155.5
Variance14.419869
MonotonicityNot monotonic
2023-12-13T08:04:14.821356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8400
84.0%
1.0 496
 
5.0%
2.0 297
 
3.0%
3.0 172
 
1.7%
5.0 86
 
0.9%
4.0 51
 
0.5%
10.0 24
 
0.2%
6.0 24
 
0.2%
7.0 22
 
0.2%
0.3 21
 
0.2%
Other values (86) 279
 
2.8%
(Missing) 128
 
1.3%
ValueCountFrequency (%)
0.0 8400
84.0%
0.1 20
 
0.2%
0.2 12
 
0.1%
0.3 21
 
0.2%
0.4 10
 
0.1%
0.5 20
 
0.2%
0.6 4
 
< 0.1%
0.7 16
 
0.2%
0.8 5
 
0.1%
0.9 6
 
0.1%
ValueCountFrequency (%)
132.0 1
< 0.1%
103.0 1
< 0.1%
91.0 1
< 0.1%
83.0 1
< 0.1%
79.9 1
< 0.1%
78.0 1
< 0.1%
68.0 1
< 0.1%
66.0 1
< 0.1%
65.0 1
< 0.1%
60.0 1
< 0.1%

소반지피에스엑스좌표
Real number (ℝ)

MISSING 

Distinct5588
Distinct (%)91.0%
Missing3857
Missing (%)38.6%
Infinite0
Infinite (%)0.0%
Mean226029.72
Minimum0
Maximum2155441
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:04:14.985490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile126009.2
Q1176586.5
median218609
Q3274421
95-th percentile320629.6
Maximum2155441
Range2155441
Interquartile range (IQR)97834.5

Descriptive statistics

Standard deviation74243.585
Coefficient of variation (CV)0.32846824
Kurtosis75.158034
Mean226029.72
Median Absolute Deviation (MAD)49864
Skewness3.4561034
Sum1.3885006 × 109
Variance5.5121099 × 109
MonotonicityNot monotonic
2023-12-13T08:04:15.376163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
0.2%
163832.0 4
 
< 0.1%
112424.0 4
 
< 0.1%
202918.3599 4
 
< 0.1%
184095.0 4
 
< 0.1%
239545.0 4
 
< 0.1%
221959.0 4
 
< 0.1%
273918.0 3
 
< 0.1%
220237.0 3
 
< 0.1%
310043.0 3
 
< 0.1%
Other values (5578) 6093
60.9%
(Missing) 3857
38.6%
ValueCountFrequency (%)
0.0 17
0.2%
126.9138 1
 
< 0.1%
127.0179 1
 
< 0.1%
127.1926 1
 
< 0.1%
127.3633 1
 
< 0.1%
127.3639 1
 
< 0.1%
127.3651 1
 
< 0.1%
127.5807 1
 
< 0.1%
127.928 1
 
< 0.1%
128.0562 1
 
< 0.1%
ValueCountFrequency (%)
2155441.0 1
< 0.1%
481055.0 2
< 0.1%
479297.0 2
< 0.1%
478886.0 2
< 0.1%
478675.0 1
< 0.1%
478248.0 2
< 0.1%
477691.0 1
< 0.1%
476253.0 1
< 0.1%
475643.0 1
< 0.1%
474879.0 1
< 0.1%

소반지피에스와이좌표
Real number (ℝ)

MISSING 

Distinct5605
Distinct (%)91.2%
Missing3857
Missing (%)38.6%
Infinite0
Infinite (%)0.0%
Mean384977.25
Minimum0
Maximum662933
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:04:15.500317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile150991.89
Q1313302.5
median426031
Q3475470
95-th percentile519051.9
Maximum662933
Range662933
Interquartile range (IQR)162167.5

Descriptive statistics

Standard deviation116251.42
Coefficient of variation (CV)0.30196958
Kurtosis-0.0073322488
Mean384977.25
Median Absolute Deviation (MAD)66507
Skewness-0.91641684
Sum2.3649152 × 109
Variance1.3514393 × 1010
MonotonicityNot monotonic
2023-12-13T08:04:15.622276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
0.2%
142123.0 4
 
< 0.1%
522590.0 4
 
< 0.1%
46411.0 4
 
< 0.1%
486627.0 4
 
< 0.1%
379094.5448 4
 
< 0.1%
323006.0 4
 
< 0.1%
487782.0 3
 
< 0.1%
483797.0 3
 
< 0.1%
482369.0 3
 
< 0.1%
Other values (5595) 6093
60.9%
(Missing) 3857
38.6%
ValueCountFrequency (%)
0.0 17
0.2%
37.3242 1
 
< 0.1%
37.3699 1
 
< 0.1%
37.3988 1
 
< 0.1%
37.4085 1
 
< 0.1%
37.4138 1
 
< 0.1%
37.4243 1
 
< 0.1%
37.6114 1
 
< 0.1%
38.106 1
 
< 0.1%
38.1141 1
 
< 0.1%
ValueCountFrequency (%)
662933.0 1
< 0.1%
560849.0 1
< 0.1%
560664.0 1
< 0.1%
542114.0 2
< 0.1%
541011.0 1
< 0.1%
540998.0 1
< 0.1%
540821.0 1
< 0.1%
538563.0 1
< 0.1%
538455.0 2
< 0.1%
538042.0 2
< 0.1%

신청서번호
Real number (ℝ)

MISSING 

Distinct303
Distinct (%)50.8%
Missing9403
Missing (%)94.0%
Infinite0
Infinite (%)0.0%
Mean11207.273
Minimum10016
Maximum12762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:04:15.742063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10016
5-th percentile10135.2
Q110554
median11294
Q311791
95-th percentile12230.2
Maximum12762
Range2746
Interquartile range (IQR)1237

Descriptive statistics

Standard deviation669.72023
Coefficient of variation (CV)0.059757644
Kurtosis-1.0965475
Mean11207.273
Median Absolute Deviation (MAD)550
Skewness-0.12286185
Sum6690742
Variance448525.19
MonotonicityNot monotonic
2023-12-13T08:04:15.875501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10391 27
 
0.3%
10016 17
 
0.2%
11887 13
 
0.1%
10412 12
 
0.1%
11826 11
 
0.1%
10132 10
 
0.1%
11097 8
 
0.1%
11511 6
 
0.1%
11771 6
 
0.1%
11352 6
 
0.1%
Other values (293) 481
 
4.8%
(Missing) 9403
94.0%
ValueCountFrequency (%)
10016 17
0.2%
10046 1
 
< 0.1%
10100 1
 
< 0.1%
10111 1
 
< 0.1%
10132 10
0.1%
10136 1
 
< 0.1%
10144 3
 
< 0.1%
10155 4
 
< 0.1%
10158 2
 
< 0.1%
10165 1
 
< 0.1%
ValueCountFrequency (%)
12762 1
< 0.1%
12573 1
< 0.1%
12490 1
< 0.1%
12484 1
< 0.1%
12466 1
< 0.1%
12464 1
< 0.1%
12413 1
< 0.1%
12405 1
< 0.1%
12402 1
< 0.1%
12370 1
< 0.1%

사용여부
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
True
10000 
ValueCountFrequency (%)
True 10000
100.0%
2023-12-13T08:04:15.998209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct438
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2009-03-18 00:00:00
Maximum2018-10-08 00:00:00
2023-12-13T08:04:16.089413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:16.229314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct501
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2009-03-20 00:00:00
Maximum2018-10-08 00:00:00
2023-12-13T08:04:16.346141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:16.463305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-13T08:04:09.588882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:02.429732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:03.738794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:04.677423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:05.443334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:06.194841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:06.916849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:07.647405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:08.733006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:09.695992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:02.565692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:03.837942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:04.773792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:05.546634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:06.274699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:06.999791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:07.722454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:08.818905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:09.794429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:02.676716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:03.939764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:04.858428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:05.636265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:06.359489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:07.077795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:07.803694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:08.923473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:09.875160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:02.777963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:04.048873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:04.929110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:05.718142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:06.449879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:07.149463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:07.878442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:09.005992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:09.952497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:02.922667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:04.188538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:05.024457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:05.809549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:06.536747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:07.237616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:07.962135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:09.120200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:10.053072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:03.349179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:04.298462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:05.118739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:05.890141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:06.614439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:07.321311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:08.045717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:09.220939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:10.144114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:03.460162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:04.410444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:05.197426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:05.972335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:06.689092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:07.400029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:08.127691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:09.316857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:10.228473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:03.563768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:04.507166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:05.277936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:06.049407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:06.764107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:07.481881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:08.551980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:09.413009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:10.332531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:03.656361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:04.596769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:05.359115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:06.127524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:06.841811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:07.557724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:08.636660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:09.506522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:04:16.556957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력관리번호차기번호경영계획구아이디입목지면적미립목지면적제지면적소반지피에스엑스좌표소반지피에스와이좌표신청서번호
이력관리번호1.0000.1450.1680.1040.0000.0000.0990.2040.381
차기번호0.1451.0000.3530.0370.0000.0000.1660.3970.336
경영계획구아이디0.1680.3531.0000.0380.0000.0510.2300.5960.280
입목지면적0.1040.0370.0381.0000.0000.0000.0000.1380.021
미립목지면적0.0000.0000.0000.0001.0000.0000.0000.1050.131
제지면적0.0000.0000.0510.0000.0001.0000.0000.0890.000
소반지피에스엑스좌표0.0990.1660.2300.0000.0000.0001.0000.5000.214
소반지피에스와이좌표0.2040.3970.5960.1380.1050.0890.5001.0000.491
신청서번호0.3810.3360.2800.0210.1310.0000.2140.4911.000
2023-12-13T08:04:16.664521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력관리번호차기번호경영계획구아이디입목지면적미립목지면적제지면적소반지피에스엑스좌표소반지피에스와이좌표신청서번호
이력관리번호1.0000.1720.1290.073-0.006-0.0140.0730.0550.427
차기번호0.1721.000-0.089-0.027-0.012-0.0100.0550.0550.201
경영계획구아이디0.129-0.0891.000-0.0510.0360.000-0.119-0.432-0.094
입목지면적0.073-0.027-0.0511.000-0.0980.264-0.0360.0380.110
미립목지면적-0.006-0.0120.036-0.0981.0000.001-0.023-0.056-0.055
제지면적-0.014-0.0100.0000.2640.0011.000-0.098-0.079-0.026
소반지피에스엑스좌표0.0730.055-0.119-0.036-0.023-0.0981.0000.0330.032
소반지피에스와이좌표0.0550.055-0.4320.038-0.056-0.0790.0331.000-0.099
신청서번호0.4270.201-0.0940.110-0.055-0.0260.032-0.0991.000

Missing values

2023-12-13T08:04:10.512157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:04:10.750794image/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.
2023-12-13T08:04:10.935106image/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

경영계획부번호이력관리번호차기번호경영계획구아이디임반아이디소반아이디입목지면적미립목지면적제지면적소반지피에스엑스좌표소반지피에스와이좌표신청서번호사용여부최초등록일시최종수정일시
51684142_8_008-000_012-00228142008-000012-0025.00.00.0221532.0201517.0<NA>Y2016-04-142016-04-14
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경영계획부번호이력관리번호차기번호경영계획구아이디임반아이디소반아이디입목지면적미립목지면적제지면적소반지피에스엑스좌표소반지피에스와이좌표신청서번호사용여부최초등록일시최종수정일시
64322104_6_045-000_006-00016104045-000006-0006.00.00.0<NA><NA><NA>Y2016-04-112016-04-11
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87733119_8_061-000_007-00018119061-000007-0002.00.00.0<NA><NA><NA>Y2016-04-142016-04-14
774850_8_022-000_032-0002850022-000032-00021.00.01.0<NA><NA><NA>Y2013-07-242013-07-24