Overview

Dataset statistics

Number of variables19
Number of observations10000
Missing cells12819
Missing cells (%)6.7%
Duplicate rows11
Duplicate rows (%)0.1%
Total size in memory1.6 MiB
Average record size in memory164.0 B

Variable types

Numeric1
Text2
Unsupported2
Categorical13
Boolean1

Dataset

Description국토지리정보원의 항공사진 관련 메타데이터 중 정사영상 성과내역 입니다. (5000도엽번호, 5000도엽명, 축척, 원점구분, 제작년월일 등)
Author국토교통부 국토지리정보원
URLhttps://www.data.go.kr/data/15067667/fileData.do

Alerts

제작기관 has constant value ""Constant
Dataset has 11 (0.1%) duplicate rowsDuplicates
작업기관 has a high cardinality: 51 distinct valuesHigh cardinality
예비 is highly overall correlated with 도엽번호5000 and 10 other fieldsHigh correlation
참고사항 is highly overall correlated with 도엽번호5000 and 12 other fieldsHigh correlation
칼라구분 is highly overall correlated with 지상표본거리 and 6 other fieldsHigh correlation
원시영상자료유무 is highly overall correlated with 작업기관 and 2 other fieldsHigh correlation
구축구분코드 is highly overall correlated with 작업기관 and 5 other fieldsHigh correlation
작업기관 is highly overall correlated with 지상표본거리 and 11 other fieldsHigh correlation
사용수치표고 is highly overall correlated with 작업기관 and 6 other fieldsHigh correlation
카메라종류 is highly overall correlated with 지상표본거리 and 9 other fieldsHigh correlation
지상표본거리 is highly overall correlated with 작업기관 and 6 other fieldsHigh correlation
해상도완화지역여부 is highly overall correlated with 작업기관 and 6 other fieldsHigh correlation
원시지리좌표계 is highly overall correlated with 지상표본거리 and 7 other fieldsHigh correlation
지리좌표계 is highly overall correlated with 도엽번호5000 and 12 other fieldsHigh correlation
원점구분 is highly overall correlated with 작업기관 and 6 other fieldsHigh correlation
도엽번호5000 is highly overall correlated with 참고사항 and 2 other fieldsHigh correlation
지상표본거리 is highly imbalanced (76.7%)Imbalance
원점구분 is highly imbalanced (54.9%)Imbalance
원시영상자료유무 is highly imbalanced (85.7%)Imbalance
참고사항 is highly imbalanced (97.6%)Imbalance
예비 is highly imbalanced (97.6%)Imbalance
칼라구분 is highly imbalanced (82.8%)Imbalance
지리좌표계 is highly imbalanced (88.7%)Imbalance
원시지리좌표계 is highly imbalanced (80.6%)Imbalance
구축구분코드 is highly imbalanced (75.2%)Imbalance
해상도완화지역여부 is highly imbalanced (85.0%)Imbalance
도엽명5000 has 248 (2.5%) missing valuesMissing
축적 has 247 (2.5%) missing valuesMissing
원시영상자료유무 has 2521 (25.2%) missing valuesMissing
워성종류 has 9803 (98.0%) missing valuesMissing
축적 is an unsupported type, check if it needs cleaning or further analysisUnsupported
워성종류 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 14:25:05.038302
Analysis finished2023-12-12 14:25:07.514359
Duration2.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도엽번호5000
Real number (ℝ)

HIGH CORRELATION 

Distinct7928
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36430546
Minimum32514040
Maximum38815100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:25:07.602636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32514040
5-th percentile34605095
Q135706092
median36616072
Q337612006
95-th percentile37815004
Maximum38815100
Range6301060
Interquartile range (IQR)1905914.8

Descriptive statistics

Standard deviation1145460.5
Coefficient of variation (CV)0.031442308
Kurtosis-0.53942582
Mean36430546
Median Absolute Deviation (MAD)914069.5
Skewness-0.24579447
Sum3.6430546 × 1011
Variance1.3120797 × 1012
MonotonicityNot monotonic
2023-12-12T23:25:07.793571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35701064 8
 
0.1%
35701075 5
 
0.1%
37702075 5
 
0.1%
36616010 5
 
0.1%
35701083 5
 
0.1%
35701076 4
 
< 0.1%
37711012 4
 
< 0.1%
37610010 4
 
< 0.1%
35803093 4
 
< 0.1%
37703033 4
 
< 0.1%
Other values (7918) 9952
99.5%
ValueCountFrequency (%)
32514040 1
< 0.1%
33602001 1
< 0.1%
33602002 1
< 0.1%
33602013 1
< 0.1%
33602016 1
< 0.1%
33602023 2
< 0.1%
33602024 1
< 0.1%
33602033 1
< 0.1%
33604007 1
< 0.1%
33604008 2
< 0.1%
ValueCountFrequency (%)
38815100 1
< 0.1%
38815094 1
< 0.1%
38815091 1
< 0.1%
38815085 1
< 0.1%
38815082 1
< 0.1%
38815077 1
< 0.1%
38815067 1
< 0.1%
38815064 1
< 0.1%
38815057 2
< 0.1%
38815056 1
< 0.1%

도엽명5000
Text

MISSING 

Distinct7646
Distinct (%)78.4%
Missing248
Missing (%)2.5%
Memory size156.2 KiB
2023-12-12T23:25:08.116180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.9410377
Min length2

Characters and Unicode

Total characters48185
Distinct characters190
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5987 ?
Unique (%)61.4%

Sample

1st row욕지033
2nd row서천018
3rd row보은064
4th row안양070
5th row보령066
ValueCountFrequency (%)
목포 19
 
0.2%
산청 17
 
0.2%
화원 14
 
0.1%
운봉 14
 
0.1%
함양 14
 
0.1%
임실 12
 
0.1%
하의 12
 
0.1%
모슬포 10
 
0.1%
제주 10
 
0.1%
성산 9
 
0.1%
Other values (7636) 9621
98.7%
2023-12-12T23:25:08.592514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11319
23.5%
1 2069
 
4.3%
3 1933
 
4.0%
4 1908
 
4.0%
7 1901
 
3.9%
5 1901
 
3.9%
2 1898
 
3.9%
6 1861
 
3.9%
8 1861
 
3.9%
9 1842
 
3.8%
Other values (180) 19692
40.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28493
59.1%
Other Letter 19684
40.9%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 1008
 
5.1%
양 928
 
4.7%
천 868
 
4.4%
주 807
 
4.1%
안 643
 
3.3%
원 477
 
2.4%
성 443
 
2.3%
영 409
 
2.1%
청 394
 
2.0%
동 329
 
1.7%
Other values (168) 13378
68.0%
Decimal Number
ValueCountFrequency (%)
0 11319
39.7%
1 2069
 
7.3%
3 1933
 
6.8%
4 1908
 
6.7%
7 1901
 
6.7%
5 1901
 
6.7%
2 1898
 
6.7%
6 1861
 
6.5%
8 1861
 
6.5%
9 1842
 
6.5%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28501
59.1%
Hangul 19684
40.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 1008
 
5.1%
양 928
 
4.7%
천 868
 
4.4%
주 807
 
4.1%
안 643
 
3.3%
원 477
 
2.4%
성 443
 
2.3%
영 409
 
2.1%
청 394
 
2.0%
동 329
 
1.7%
Other values (168) 13378
68.0%
Common
ValueCountFrequency (%)
0 11319
39.7%
1 2069
 
7.3%
3 1933
 
6.8%
4 1908
 
6.7%
7 1901
 
6.7%
5 1901
 
6.7%
2 1898
 
6.7%
6 1861
 
6.5%
8 1861
 
6.5%
9 1842
 
6.5%
Other values (2) 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28501
59.1%
Hangul 19684
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11319
39.7%
1 2069
 
7.3%
3 1933
 
6.8%
4 1908
 
6.7%
7 1901
 
6.7%
5 1901
 
6.7%
2 1898
 
6.7%
6 1861
 
6.5%
8 1861
 
6.5%
9 1842
 
6.5%
Other values (2) 8
 
< 0.1%
Hangul
ValueCountFrequency (%)
산 1008
 
5.1%
양 928
 
4.7%
천 868
 
4.4%
주 807
 
4.1%
안 643
 
3.3%
원 477
 
2.4%
성 443
 
2.3%
영 409
 
2.1%
청 394
 
2.0%
동 329
 
1.7%
Other values (168) 13378
68.0%

축적
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing247
Missing (%)2.5%
Memory size156.2 KiB

지상표본거리
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0.25
9247 
0.51
 
451
0.12
 
289
1.0
 
13

Length

Max length4
Median length4
Mean length3.9987
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.25
2nd row0.25
3rd row0.25
4th row0.25
5th row0.25

Common Values

ValueCountFrequency (%)
0.25 9247
92.5%
0.51 451
 
4.5%
0.12 289
 
2.9%
1.0 13
 
0.1%

Length

2023-12-12T23:25:08.752941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:08.857677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.25 9247
92.5%
0.51 451
 
4.5%
0.12 289
 
2.9%
1.0 13
 
0.1%

제작기관
Categorical

CONSTANT 

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

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
국토지리정보원 10000
100.0%

Length

2023-12-12T23:25:08.969128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:09.084926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국토지리정보원 10000
100.0%

작업기관
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct51
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
새한항업㈜
1270 
㈜범아엔지니어링 (02-3487-0011)
645 
한국종합설계㈜
 
607
중앙항업(주)
 
606
삼아항업
 
589
Other values (46)
6283 

Length

Max length30
Median length28
Mean length7.748
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row㈜범아엔지니어링 (02-3487-0011)
2nd row삼아항업
3rd row공간정보기술(주)
4th row명화지리정보(주)
5th row한양지에스티

Common Values

ValueCountFrequency (%)
새한항업㈜ 1270
 
12.7%
㈜범아엔지니어링 (02-3487-0011) 645
 
6.5%
한국종합설계㈜ 607
 
6.1%
중앙항업(주) 606
 
6.1%
삼아항업 589
 
5.9%
중앙항업㈜ 564
 
5.6%
공간정보기술(주) 544
 
5.4%
한양지에스티 358
 
3.6%
천우항측㈜ 339
 
3.4%
네이버시스템㈜ 320
 
3.2%
Other values (41) 4158
41.6%

Length

2023-12-12T23:25:09.210217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
새한항업㈜ 1270
 
11.6%
㈜범아엔지니어링 886
 
8.1%
02-3487-0011 645
 
5.9%
한국종합설계㈜ 607
 
5.5%
중앙항업(주 606
 
5.5%
삼아항업 589
 
5.4%
중앙항업㈜ 564
 
5.2%
공간정보기술(주 544
 
5.0%
한양지에스티 358
 
3.3%
천우항측㈜ 339
 
3.1%
Other values (41) 4535
41.4%

원점구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
중부
6151 
동부
3733 
서부
 
93
㈜한양지에스티
 
13
동해
 
10

Length

Max length7
Median length2
Mean length2.0065
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동부
2nd row중부
3rd row중부
4th row중부
5th row중부

Common Values

ValueCountFrequency (%)
중부 6151
61.5%
동부 3733
37.3%
서부 93
 
0.9%
㈜한양지에스티 13
 
0.1%
동해 10
 
0.1%

Length

2023-12-12T23:25:09.355484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:09.499448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중부 6151
61.5%
동부 3733
37.3%
서부 93
 
0.9%
㈜한양지에스티 13
 
0.1%
동해 10
 
0.1%

제작년월일
Text

Distinct102
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T23:25:09.841201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.5772
Min length2

Characters and Unicode

Total characters95772
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2014-01-24
2nd row2012-07-13
3rd row2016-12-15
4th row2018-11-29
5th row2011-10-26
ValueCountFrequency (%)
2012-07-13 1329
 
13.3%
2014-12-26 774
 
7.7%
2014-01-24 629
 
6.3%
2011-11-14 589
 
5.9%
522
 
5.2%
2011-11-04 469
 
4.7%
2011-10-26 358
 
3.6%
2015-11-27 256
 
2.6%
2016-12-15 254
 
2.5%
2017-12-01 195
 
1.9%
Other values (92) 4625
46.2%
2023-12-12T23:25:10.306689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 24649
25.7%
- 20000
20.9%
2 17541
18.3%
0 16737
17.5%
4 3769
 
3.9%
7 3541
 
3.7%
6 2436
 
2.5%
3 2208
 
2.3%
5 1733
 
1.8%
8 1708
 
1.8%
Other values (3) 1450
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 75720
79.1%
Dash Punctuation 20000
 
20.9%
Other Letter 52
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 24649
32.6%
2 17541
23.2%
0 16737
22.1%
4 3769
 
5.0%
7 3541
 
4.7%
6 2436
 
3.2%
3 2208
 
2.9%
5 1733
 
2.3%
8 1708
 
2.3%
9 1398
 
1.8%
Other Letter
ValueCountFrequency (%)
중 26
50.0%
부 26
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 95720
99.9%
Hangul 52
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 24649
25.8%
- 20000
20.9%
2 17541
18.3%
0 16737
17.5%
4 3769
 
3.9%
7 3541
 
3.7%
6 2436
 
2.5%
3 2208
 
2.3%
5 1733
 
1.8%
8 1708
 
1.8%
Hangul
ValueCountFrequency (%)
중 26
50.0%
부 26
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95720
99.9%
Hangul 52
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 24649
25.8%
- 20000
20.9%
2 17541
18.3%
0 16737
17.5%
4 3769
 
3.9%
7 3541
 
3.7%
6 2436
 
2.5%
3 2208
 
2.3%
5 1733
 
1.8%
8 1708
 
1.8%
Hangul
ValueCountFrequency (%)
중 26
50.0%
부 26
50.0%

원시영상자료유무
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing2521
Missing (%)25.2%
Memory size97.7 KiB
True
7327 
False
 
152
(Missing)
2521 
ValueCountFrequency (%)
True 7327
73.3%
False 152
 
1.5%
(Missing) 2521
 
25.2%
2023-12-12T23:25:10.429863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

참고사항
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9963 
없음.
 
24
SEC001
 
13

Length

Max length6
Median length4
Mean length4.0002
Min length3

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> 9963
99.6%
없음. 24
 
0.2%
SEC001 13
 
0.1%

Length

2023-12-12T23:25:10.554396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:10.688269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9963
99.6%
없음 24
 
0.2%
sec001 13
 
0.1%

예비
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9976 
없음.
 
24

Length

Max length4
Median length4
Mean length3.9976
Min length3

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> 9976
99.8%
없음. 24
 
0.2%

Length

2023-12-12T23:25:10.801913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:10.938088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9976
99.8%
없음 24
 
0.2%

칼라구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
컬러
9550 
흑백
 
437
<NA>
 
13

Length

Max length4
Median length2
Mean length2.0026
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
컬러 9550
95.5%
흑백 437
 
4.4%
<NA> 13
 
0.1%

Length

2023-12-12T23:25:11.072729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:11.222483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
컬러 9550
95.5%
흑백 437
 
4.4%
na 13
 
0.1%

사용수치표고
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
DEM
6065 
등고선
3922 
컬러
 
13

Length

Max length3
Median length3
Mean length2.9987
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEM
2nd row등고선
3rd rowDEM
4th rowDEM
5th row등고선

Common Values

ValueCountFrequency (%)
DEM 6065
60.7%
등고선 3922
39.2%
컬러 13
 
0.1%

Length

2023-12-12T23:25:11.363735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:11.458666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
dem 6065
60.7%
등고선 3922
39.2%
컬러 13
 
0.1%

카메라종류
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
DMC
2483 
<NA>
2246 
ADS80
2016 
DMCI
780 
UltraCam Xp
382 
Other values (30)
2093 

Length

Max length15
Median length11
Mean length4.787
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd rowDMC
3rd rowADS80
4th rowADS80
5th rowADS80

Common Values

ValueCountFrequency (%)
DMC 2483
24.8%
<NA> 2246
22.5%
ADS80 2016
20.2%
DMCI 780
 
7.8%
UltraCam Xp 382
 
3.8%
Ultracamxp 333
 
3.3%
DMC2 259
 
2.6%
UltraCam 250
 
2.5%
ULTRACAM 187
 
1.9%
DMCⅡ 168
 
1.7%
Other values (25) 896
 
9.0%

Length

2023-12-12T23:25:11.582471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dmc 2502
24.0%
na 2246
21.6%
ads80 2016
19.3%
ultracam 819
 
7.9%
dmci 780
 
7.5%
xp 382
 
3.7%
ultracamxp 341
 
3.3%
dmc2 259
 
2.5%
dmcⅱ 168
 
1.6%
dmcii 143
 
1.4%
Other values (24) 765
 
7.3%

워성종류
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9803
Missing (%)98.0%
Memory size156.2 KiB

지리좌표계
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9849 
<NA>
 
151

Length

Max length4
Median length1
Mean length1.0453
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 9849
98.5%
<NA> 151
 
1.5%

Length

2023-12-12T23:25:11.728071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:11.831844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9849
98.5%
na 151
 
1.5%

원시지리좌표계
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9418 
2
 
305
1
 
259
3
 
18

Length

Max length4
Median length4
Mean length3.8254
Min length1

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> 9418
94.2%
2 305
 
3.0%
1 259
 
2.6%
3 18
 
0.2%

Length

2023-12-12T23:25:11.937278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:12.041242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9418
94.2%
2 305
 
3.0%
1 259
 
2.6%
3 18
 
0.2%

구축구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
PDT004
9251 
PDT001
 
736
<NA>
 
13

Length

Max length6
Median length6
Mean length5.9974
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPDT004
2nd rowPDT004
3rd rowPDT004
4th rowPDT004
5th rowPDT004

Common Values

ValueCountFrequency (%)
PDT004 9251
92.5%
PDT001 736
 
7.4%
<NA> 13
 
0.1%

Length

2023-12-12T23:25:12.406932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:12.522361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pdt004 9251
92.5%
pdt001 736
 
7.4%
na 13
 
0.1%

해상도완화지역여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
N
9629 
Y
 
358
PDT004
 
13

Length

Max length6
Median length1
Mean length1.0065
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
N 9629
96.3%
Y 358
 
3.6%
PDT004 13
 
0.1%

Length

2023-12-12T23:25:12.633311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:12.727983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 9629
96.3%
y 358
 
3.6%
pdt004 13
 
0.1%

Interactions

2023-12-12T23:25:06.792578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:25:12.811123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도엽번호5000지상표본거리작업기관원점구분원시영상자료유무참고사항칼라구분사용수치표고카메라종류원시지리좌표계구축구분코드해상도완화지역여부
도엽번호50001.0000.2870.8480.5940.3101.0000.1190.2510.6650.5950.2610.623
지상표본거리0.2871.0000.9570.1390.027NaN0.9990.1710.9990.8380.1190.007
작업기관0.8480.9571.0000.8600.9460.9950.8420.9770.9600.9830.8890.927
원점구분0.5940.1390.8601.0000.2280.9950.2530.7340.8170.4960.1500.720
원시영상자료유무0.3100.0270.9460.2281.000NaN0.0470.1590.4120.0000.0670.135
참고사항1.000NaN0.9950.995NaN1.000NaN0.995NaN0.995NaN0.995
칼라구분0.1190.9990.8420.2530.047NaN1.0000.2660.9951.0000.0920.052
사용수치표고0.2510.1710.9770.7340.1590.9950.2661.0000.9740.0960.3410.943
카메라종류0.6650.9990.9600.8170.412NaN0.9950.9741.0000.9730.7740.896
원시지리좌표계0.5950.8380.9830.4960.0000.9951.0000.0960.9731.000NaN0.499
구축구분코드0.2610.1190.8890.1500.067NaN0.0920.3410.774NaN1.0000.151
해상도완화지역여부0.6230.0070.9270.7200.1350.9950.0520.9430.8960.4990.1511.000
2023-12-12T23:25:12.981310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예비참고사항칼라구분원시영상자료유무구축구분코드작업기관사용수치표고카메라종류지상표본거리해상도완화지역여부원시지리좌표계지리좌표계원점구분
예비1.0001.0001.000NaN1.0001.0001.000NaN1.0001.0001.0001.0001.000
참고사항1.0001.0001.0001.0001.0000.9390.9391.0001.0000.9390.9391.0000.939
칼라구분1.0001.0001.0000.0300.0590.7350.1710.9930.9690.0330.9991.0000.168
원시영상자료유무NaN1.0000.0301.0000.0430.8340.2620.3270.0450.2220.0001.0000.278
구축구분코드1.0001.0000.0590.0431.0000.7900.2210.6780.0790.0971.0001.0000.099
작업기관1.0000.9390.7350.8340.7901.0000.8980.5530.8400.7730.9721.0000.569
사용수치표고1.0000.9390.1710.2620.2210.8981.0000.9050.1620.7070.1581.0000.729
카메라종류NaN1.0000.9930.3270.6780.5530.9051.0000.9710.7310.8521.0000.549
지상표본거리1.0001.0000.9690.0450.0790.8400.1620.9711.0000.0060.9071.0000.113
해상도완화지역여부1.0000.9390.0330.2220.0970.7730.7070.7310.0061.0000.2021.0000.710
원시지리좌표계1.0000.9390.9990.0001.0000.9720.1580.8520.9070.2021.0001.0000.201
지리좌표계1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
원점구분1.0000.9390.1680.2780.0990.5690.7290.5490.1130.7100.2011.0001.000
2023-12-12T23:25:13.178426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도엽번호5000지상표본거리작업기관원점구분원시영상자료유무참고사항예비칼라구분사용수치표고카메라종류지리좌표계원시지리좌표계구축구분코드해상도완화지역여부
도엽번호50001.0000.1310.4890.4210.2320.9711.0000.0890.1640.3071.0000.4860.1950.494
지상표본거리0.1311.0000.8400.1130.0451.0001.0000.9690.1620.9711.0000.9070.0790.006
작업기관0.4890.8401.0000.5690.8340.9391.0000.7350.8980.5531.0000.9720.7900.773
원점구분0.4210.1130.5691.0000.2780.9391.0000.1680.7290.5491.0000.2010.0990.710
원시영상자료유무0.2320.0450.8340.2781.0001.0000.0000.0300.2620.3271.0000.0000.0430.222
참고사항0.9711.0000.9390.9391.0001.0001.0001.0000.9391.0001.0000.9391.0000.939
예비1.0001.0001.0001.0000.0001.0001.0001.0001.0000.0001.0001.0001.0001.000
칼라구분0.0890.9690.7350.1680.0301.0001.0001.0000.1710.9931.0000.9990.0590.033
사용수치표고0.1640.1620.8980.7290.2620.9391.0000.1711.0000.9051.0000.1580.2210.707
카메라종류0.3070.9710.5530.5490.3271.0000.0000.9930.9051.0001.0000.8520.6780.731
지리좌표계1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
원시지리좌표계0.4860.9070.9720.2010.0000.9391.0000.9990.1580.8521.0001.0001.0000.202
구축구분코드0.1950.0790.7900.0990.0431.0001.0000.0590.2210.6781.0001.0001.0000.097
해상도완화지역여부0.4940.0060.7730.7100.2220.9391.0000.0330.7070.7311.0000.2020.0971.000

Missing values

2023-12-12T23:25:06.934402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:25:07.158492image/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.
2023-12-12T23:25:07.354080image/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

도엽번호5000도엽명5000축적지상표본거리제작기관작업기관원점구분제작년월일원시영상자료유무참고사항예비칼라구분사용수치표고카메라종류워성종류지리좌표계원시지리좌표계구축구분코드해상도완화지역여부
7101634806033욕지03350000.25국토지리정보원㈜범아엔지니어링 (02-3487-0011)동부2014-01-24<NA><NA><NA>컬러DEM<NA>NaN1<NA>PDT004N
8145136615018서천01850000.25국토지리정보원삼아항업중부2012-07-13Y<NA><NA>컬러등고선DMCNaN1<NA>PDT004N
2342636711064보은06450000.25국토지리정보원공간정보기술(주)중부2016-12-15Y<NA><NA>컬러DEMADS80NaN1<NA>PDT004N
3898537612070안양07050000.25국토지리정보원명화지리정보(주)중부2018-11-29Y<NA><NA>컬러DEMADS80NaN1<NA>PDT004N
8028536611066보령06650000.25국토지리정보원한양지에스티중부2011-10-26<NA><NA><NA>컬러등고선ADS80NaN1<NA>PDT004N
4471037814047<NA>50000.25국토지리정보원한양지에스티동부2011-10-26<NA><NA><NA>컬러등고선ADS80NaN1<NA>PDT004N
7580635810082남지08250000.25국토지리정보원한국종합설계㈜동부2012-07-13Y<NA><NA>컬러등고선DMCNaN1<NA>PDT004N
126736701027평택02750000.25국토지리정보원㈜아세아항측중부2014-12-26<NA><NA><NA>컬러DEM<NA>NaN1<NA>PDT004N
3605137703001홍천02250000.25국토지리정보원㈜한양지에스티중부2018-08-03Y<NA><NA>컬러DEMDMCIINaN1<NA>PDT004N
7808135815034마산034NaN0.25국토지리정보원한진정보통신(주) 컨소시엄(02-2166-7436)동부2009-02-00<NA><NA><NA>컬러DEM<NA>NaN12PDT004N
도엽번호5000도엽명5000축적지상표본거리제작기관작업기관원점구분제작년월일원시영상자료유무참고사항예비칼라구분사용수치표고카메라종류워성종류지리좌표계원시지리좌표계구축구분코드해상도완화지역여부
6596435701018전주01850000.25국토지리정보원새한항업㈜중부2011-11-04Y<NA><NA>컬러등고선DMCNaN1<NA>PDT004N
6091534602030목포03050000.25국토지리정보원새한항업㈜중부2011-11-04Y<NA><NA>컬러등고선DMCNaN1<NA>PDT004N
6153934606005화원00550000.25국토지리정보원새한항업㈜중부2011-11-04Y<NA><NA>컬러등고선DMCNaN1<NA>PDT004N
3768334611018완도01850000.25국토지리정보원(주)올포랜드중부2017-12-12Y<NA><NA>컬러DEMDMCⅡNaN1<NA>PDT004N
9397937702010일동01050000.51국토지리정보원㈜엔지엘중부2019-12-16Y<NA><NA>흑백DEMRMKTOP 15NaN1<NA>PDT004N
7608735807048청도04850000.25국토지리정보원㈜범아엔지니어링 (02-3487-0011)동부2014-01-24<NA><NA><NA>컬러DEM<NA>NaN1<NA>PDT004N
4696537703070춘천07050000.25국토지리정보원중앙항업(주)중부--<NA><NA><NA>컬러DEM<NA>NaN1<NA>PDT004N
3284135801006가야00650000.25국토지리정보원명화지리정보㈜동부2017-11-23Y<NA><NA>컬러DEMDMCINaN1<NA>PDT004N
7344836605017근흥01750000.25국토지리정보원삼아항업중부2012-07-13Y<NA><NA>컬러등고선DMCNaN1<NA>PDT004N
2093435712024산청02450000.25국토지리정보원천우항측㈜중부2016-12-15Y<NA><NA>컬러DEMADS80NaN1<NA>PDT004N

Duplicate rows

Most frequently occurring

도엽번호5000도엽명5000지상표본거리제작기관작업기관원점구분제작년월일원시영상자료유무참고사항예비칼라구분사용수치표고카메라종류지리좌표계원시지리좌표계구축구분코드해상도완화지역여부# duplicates
034702010순천0100.51국토지리정보원범아엔지니어링중부2017-03-10Y<NA><NA>흑백DEMRC10<NA><NA>PDT004N2
134702049순천0490.51국토지리정보원범아엔지니어링중부2017-03-10Y<NA><NA>흑백DEMRC10<NA><NA>PDT004N2
235701052전주0520.51국토지리정보원㈜신한항업중부2017-04-06Y<NA><NA>흑백DEMRC-8<NA><NA>PDT004N2
335701064전주0640.51국토지리정보원(주)신한항업중부2017-04-06Y<NA><NA>흑백DEMRC-81<NA>PDT004N2
435701069전주0690.51국토지리정보원㈜신한항업중부2017-04-06Y<NA><NA>흑백DEMRC-8<NA><NA>PDT004N2
535701073전주0730.51국토지리정보원(주)신한항업중부2017-04-06Y<NA><NA>흑백DEMRC-101<NA>PDT004N2
635701075전주0750.51국토지리정보원㈜신한항업중부2017-04-06Y<NA><NA>흑백DEMRC-8<NA><NA>PDT004N2
735701076전주0760.51국토지리정보원㈜신한항업중부2017-04-06Y<NA><NA>흑백DEMRC-8<NA><NA>PDT004N2
835701083전주0830.51국토지리정보원㈜신한항업중부2017-04-06Y<NA><NA>흑백DEMRC-8<NA><NA>PDT004N2
935714089구례0890.51국토지리정보원범아엔지니어링중부2017-03-10Y<NA><NA>흑백DEMRC10<NA><NA>PDT004N2