Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells25774
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory161.0 B

Variable types

Text3
DateTime1
Numeric9
Unsupported1
Categorical4

Dataset

Description국토지리정보원의 항공사진 관련 메타데이터 중 항공사진촬영기록코스 입니다. (촬영일자, 렌즈번호, 코스일련번호, 코스번호 등)
Author국토교통부 국토지리정보원
URLhttps://www.data.go.kr/data/15067483/fileData.do

Alerts

대표필름번호 is highly overall correlated with 최소위성수High correlation
촬영각도 is highly overall correlated with 촬영방향High correlation
최소위성수 is highly overall correlated with 대표필름번호 and 1 other fieldsHigh correlation
진행방향중복도_최대 is highly overall correlated with 진행방향중복도_최소 and 1 other fieldsHigh correlation
진행방향중복도_최소 is highly overall correlated with 진행방향중복도_최대High correlation
촬영중복도_최대 is highly overall correlated with 촬영중복도_최소 and 1 other fieldsHigh correlation
촬영중복도_최소 is highly overall correlated with 촬영중복도_최대High correlation
노출 is highly overall correlated with 조리개High correlation
조리개 is highly overall correlated with 최소위성수 and 3 other fieldsHigh correlation
촬영방향 is highly overall correlated with 촬영각도High correlation
필터 is highly imbalanced (52.8%)Imbalance
개시시간 has 2586 (25.9%) missing valuesMissing
대표필름번호 has 5708 (57.1%) missing valuesMissing
매수 has 660 (6.6%) missing valuesMissing
촬영각도 has 2452 (24.5%) missing valuesMissing
최소위성수 has 3475 (34.8%) missing valuesMissing
진행방향중복도_최대 has 2706 (27.1%) missing valuesMissing
진행방향중복도_최소 has 2736 (27.4%) missing valuesMissing
촬영중복도_최대 has 2725 (27.3%) missing valuesMissing
촬영중복도_최소 has 2726 (27.3%) missing valuesMissing
매수 is highly skewed (γ1 = 85.22601283)Skewed
개시시간 is an unsupported type, check if it needs cleaning or further analysisUnsupported
대표필름번호 has 1665 (16.7%) zerosZeros
매수 has 462 (4.6%) zerosZeros
촬영각도 has 3289 (32.9%) zerosZeros
진행방향중복도_최대 has 484 (4.8%) zerosZeros
진행방향중복도_최소 has 484 (4.8%) zerosZeros
촬영중복도_최대 has 484 (4.8%) zerosZeros
촬영중복도_최소 has 484 (4.8%) zerosZeros

Reproduction

Analysis started2023-12-12 13:08:22.589734
Analysis finished2023-12-12 13:08:34.417562
Duration11.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct1742
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T22:08:34.608677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters110000
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

Unique352 ?
Unique (%)3.5%

Sample

1st row201704A208N
2nd row196600A0001
3rd row200804A0017
4th row201309A303C
5th row201704A511N
ValueCountFrequency (%)
202102a102c 64
 
0.6%
202102a102d 59
 
0.6%
202102a103c 52
 
0.5%
201309a301c 46
 
0.5%
202003a0203 44
 
0.4%
202102a101d 44
 
0.4%
202102a103d 44
 
0.4%
201505a301c 43
 
0.4%
201309a304c 42
 
0.4%
202102a101c 41
 
0.4%
Other values (1732) 9521
95.2%
2023-12-12T22:08:35.002303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33937
30.9%
2 17428
15.8%
1 15491
14.1%
A 10000
 
9.1%
3 6587
 
6.0%
9 4794
 
4.4%
4 4648
 
4.2%
C 4493
 
4.1%
5 3959
 
3.6%
8 2321
 
2.1%
Other values (4) 6342
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93749
85.2%
Uppercase Letter 16251
 
14.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33937
36.2%
2 17428
18.6%
1 15491
16.5%
3 6587
 
7.0%
9 4794
 
5.1%
4 4648
 
5.0%
5 3959
 
4.2%
8 2321
 
2.5%
6 2300
 
2.5%
7 2284
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
A 10000
61.5%
C 4493
27.6%
N 935
 
5.8%
D 823
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 93749
85.2%
Latin 16251
 
14.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33937
36.2%
2 17428
18.6%
1 15491
16.5%
3 6587
 
7.0%
9 4794
 
5.1%
4 4648
 
5.0%
5 3959
 
4.2%
8 2321
 
2.5%
6 2300
 
2.5%
7 2284
 
2.4%
Latin
ValueCountFrequency (%)
A 10000
61.5%
C 4493
27.6%
N 935
 
5.8%
D 823
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 110000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33937
30.9%
2 17428
15.8%
1 15491
14.1%
A 10000
 
9.1%
3 6587
 
6.0%
9 4794
 
4.4%
4 4648
 
4.2%
C 4493
 
4.1%
5 3959
 
3.6%
8 2321
 
2.1%
Other values (4) 6342
 
5.8%
Distinct2566
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1947-10-16 00:00:00
Maximum2023-04-09 00:00:00
2023-12-12T22:08:35.198230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:35.362017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct116
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T22:08:35.708950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length7.2581
Min length1

Characters and Unicode

Total characters72581
Distinct characters33
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st row15244
2nd row54
3rd row13084
4th row1311
5th rowUC-SXp-1-91514228
ValueCountFrequency (%)
1307 766
 
7.6%
1 640
 
6.4%
30107 606
 
6.0%
dmcii230-018 431
 
4.3%
1311 406
 
4.0%
dmcii250-038 379
 
3.8%
404 318
 
3.2%
13032 299
 
3.0%
uc-sxp-1-00015244 284
 
2.8%
10312058 282
 
2.8%
Other values (106) 5615
56.0%
2023-12-12T22:08:36.194341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14100
19.4%
0 11142
15.4%
3 7020
9.7%
2 5789
8.0%
- 4440
 
6.1%
5 4411
 
6.1%
4 3597
 
5.0%
8 2965
 
4.1%
C 2679
 
3.7%
7 2433
 
3.4%
Other values (23) 14005
19.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54439
75.0%
Uppercase Letter 12515
 
17.2%
Dash Punctuation 4440
 
6.1%
Lowercase Letter 1085
 
1.5%
Connector Punctuation 58
 
0.1%
Space Separator 26
 
< 0.1%
Other Punctuation 17
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 2679
21.4%
I 2137
17.1%
D 1811
14.5%
M 1782
14.2%
S 1243
9.9%
X 1225
9.8%
U 1047
 
8.4%
A 258
 
2.1%
G 193
 
1.5%
N 75
 
0.6%
Other values (4) 65
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 14100
25.9%
0 11142
20.5%
3 7020
12.9%
2 5789
10.6%
5 4411
 
8.1%
4 3597
 
6.6%
8 2965
 
5.4%
7 2433
 
4.5%
6 1590
 
2.9%
9 1392
 
2.6%
Lowercase Letter
ValueCountFrequency (%)
p 784
72.3%
e 156
 
14.4%
f 91
 
8.4%
r 54
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 4440
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 58
100.0%
Space Separator
ValueCountFrequency (%)
26
100.0%
Other Punctuation
ValueCountFrequency (%)
# 17
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58980
81.3%
Latin 13601
 
18.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 2679
19.7%
I 2137
15.7%
D 1811
13.3%
M 1782
13.1%
S 1243
9.1%
X 1225
9.0%
U 1047
 
7.7%
p 784
 
5.8%
A 258
 
1.9%
G 193
 
1.4%
Other values (9) 442
 
3.2%
Common
ValueCountFrequency (%)
1 14100
23.9%
0 11142
18.9%
3 7020
11.9%
2 5789
9.8%
- 4440
 
7.5%
5 4411
 
7.5%
4 3597
 
6.1%
8 2965
 
5.0%
7 2433
 
4.1%
6 1590
 
2.7%
Other values (4) 1493
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72580
> 99.9%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14100
19.4%
0 11142
15.4%
3 7020
9.7%
2 5789
8.0%
- 4440
 
6.1%
5 4411
 
6.1%
4 3597
 
5.0%
8 2965
 
4.1%
C 2679
 
3.7%
7 2433
 
3.4%
Other values (22) 14004
19.3%
Number Forms
ValueCountFrequency (%)
1
100.0%

코스일련번호
Real number (ℝ)

Distinct818
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.4718
Minimum1
Maximum988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:08:36.361844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q111
median38
Q3114
95-th percentile552
Maximum988
Range987
Interquartile range (IQR)103

Descriptive statistics

Standard deviation180.87023
Coefficient of variation (CV)1.6081384
Kurtosis5.9191852
Mean112.4718
Median Absolute Deviation (MAD)33
Skewness2.4813525
Sum1124718
Variance32714.038
MonotonicityNot monotonic
2023-12-12T22:08:36.577456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 364
 
3.6%
2 331
 
3.3%
3 299
 
3.0%
4 263
 
2.6%
5 250
 
2.5%
7 214
 
2.1%
6 210
 
2.1%
8 182
 
1.8%
9 169
 
1.7%
12 160
 
1.6%
Other values (808) 7558
75.6%
ValueCountFrequency (%)
1 364
3.6%
2 331
3.3%
3 299
3.0%
4 263
2.6%
5 250
2.5%
6 210
2.1%
7 214
2.1%
8 182
1.8%
9 169
1.7%
10 150
1.5%
ValueCountFrequency (%)
988 1
< 0.1%
984 1
< 0.1%
962 1
< 0.1%
961 1
< 0.1%
957 1
< 0.1%
956 1
< 0.1%
954 1
< 0.1%
952 1
< 0.1%
950 1
< 0.1%
947 1
< 0.1%
Distinct1375
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T22:08:37.040018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.4794
Min length1

Characters and Unicode

Total characters24794
Distinct characters24
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique612 ?
Unique (%)6.1%

Sample

1st row121
2nd rowD
3rd row14
4th rowA59
5th row5
ValueCountFrequency (%)
1 206
 
2.1%
2 186
 
1.9%
3 175
 
1.8%
4 172
 
1.7%
5 165
 
1.7%
6 144
 
1.4%
8 143
 
1.4%
7 138
 
1.4%
14 125
 
1.2%
13 120
 
1.2%
Other values (1365) 8426
84.3%
2023-12-12T22:08:37.682590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4038
16.3%
0 3284
13.2%
2 2908
11.7%
3 2387
9.6%
4 2058
8.3%
5 1787
7.2%
6 1611
 
6.5%
7 1514
 
6.1%
8 1352
 
5.5%
9 1270
 
5.1%
Other values (14) 2585
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22209
89.6%
Uppercase Letter 2576
 
10.4%
Other Letter 9
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 962
37.3%
B 917
35.6%
A 597
23.2%
C 71
 
2.8%
D 10
 
0.4%
E 5
 
0.2%
F 4
 
0.2%
R 4
 
0.2%
W 2
 
0.1%
G 2
 
0.1%
Other values (2) 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 4038
18.2%
0 3284
14.8%
2 2908
13.1%
3 2387
10.7%
4 2058
9.3%
5 1787
8.0%
6 1611
 
7.3%
7 1514
 
6.8%
8 1352
 
6.1%
9 1270
 
5.7%
Other Letter
ValueCountFrequency (%)
8
88.9%
1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 22209
89.6%
Latin 2576
 
10.4%
Hangul 9
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 962
37.3%
B 917
35.6%
A 597
23.2%
C 71
 
2.8%
D 10
 
0.4%
E 5
 
0.2%
F 4
 
0.2%
R 4
 
0.2%
W 2
 
0.1%
G 2
 
0.1%
Other values (2) 2
 
0.1%
Common
ValueCountFrequency (%)
1 4038
18.2%
0 3284
14.8%
2 2908
13.1%
3 2387
10.7%
4 2058
9.3%
5 1787
8.0%
6 1611
 
7.3%
7 1514
 
6.8%
8 1352
 
6.1%
9 1270
 
5.7%
Hangul
ValueCountFrequency (%)
8
88.9%
1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24785
> 99.9%
Hangul 9
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4038
16.3%
0 3284
13.2%
2 2908
11.7%
3 2387
9.6%
4 2058
8.3%
5 1787
7.2%
6 1611
 
6.5%
7 1514
 
6.1%
8 1352
 
5.5%
9 1270
 
5.1%
Other values (12) 2576
10.4%
Hangul
ValueCountFrequency (%)
8
88.9%
1
 
11.1%

개시시간
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2586
Missing (%)25.9%
Memory size156.2 KiB

필터
Categorical

IMBALANCE 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
3987 
UV
3084 
auto
1623 
Yellow
500 
yellow
 
274
Other values (22)
532 

Length

Max length8
Median length4
Mean length3.5559
Min length1

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3987
39.9%
UV 3084
30.8%
auto 1623
16.2%
Yellow 500
 
5.0%
yellow 274
 
2.7%
YELLOW 168
 
1.7%
B 55
 
0.5%
L.Y 55
 
0.5%
525NM 37
 
0.4%
MB 30
 
0.3%
Other values (17) 187
 
1.9%

Length

2023-12-12T22:08:37.862893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3987
39.9%
uv 3084
30.8%
auto 1671
16.7%
yellow 942
 
9.4%
b 66
 
0.7%
l.y 55
 
0.5%
525nm 37
 
0.4%
mb 30
 
0.3%
420nm 30
 
0.3%
l.yellow 24
 
0.2%
Other values (11) 74
 
0.7%

노출
Categorical

HIGH CORRELATION 

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
auto
3358 
<NA>
2325 
AUTO
1753 
1/500
1022 
0
473 
Other values (38)
1069 

Length

Max length5
Median length4
Mean length3.8643
Min length1

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st row1/500
2nd row<NA>
3rd rowauto
4th rowauto
5th rowAUTO

Common Values

ValueCountFrequency (%)
auto 3358
33.6%
<NA> 2325
23.2%
AUTO 1753
17.5%
1/500 1022
 
10.2%
0 473
 
4.7%
300 251
 
2.5%
350 208
 
2.1%
250 106
 
1.1%
자동 97
 
1.0%
400 78
 
0.8%
Other values (33) 329
 
3.3%

Length

2023-12-12T22:08:38.022684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
auto 5181
51.8%
na 2325
23.3%
1/500 1022
 
10.2%
0 473
 
4.7%
300 251
 
2.5%
350 208
 
2.1%
250 106
 
1.1%
자동 97
 
1.0%
400 78
 
0.8%
230 65
 
0.7%
Other values (30) 193
 
1.9%

조리개
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5459 
auto
1182 
11
864 
AUTO
622 
0
 
518
Other values (17)
1355 

Length

Max length4
Median length4
Mean length3.3431
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row11
2nd row<NA>
3rd row<NA>
4th rowauto
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 5459
54.6%
auto 1182
 
11.8%
11 864
 
8.6%
AUTO 622
 
6.2%
0 518
 
5.2%
8 505
 
5.1%
4 258
 
2.6%
5.6 235
 
2.4%
5 73
 
0.7%
6 71
 
0.7%
Other values (12) 213
 
2.1%

Length

2023-12-12T22:08:38.168925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 5459
54.6%
auto 1804
 
18.0%
11 864
 
8.6%
0 518
 
5.2%
8 505
 
5.1%
4 258
 
2.6%
5.6 235
 
2.4%
5 73
 
0.7%
6 71
 
0.7%
f8 67
 
0.7%
Other values (11) 146
 
1.5%

대표필름번호
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1761
Distinct (%)41.0%
Missing5708
Missing (%)57.1%
Infinite0
Infinite (%)0.0%
Mean1287.7761
Minimum0
Maximum30221
Zeros1665
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:08:38.615672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median129
Q31387
95-th percentile7033.45
Maximum30221
Range30221
Interquartile range (IQR)1387

Descriptive statistics

Standard deviation2338.5359
Coefficient of variation (CV)1.8159492
Kurtosis13.461396
Mean1287.7761
Median Absolute Deviation (MAD)129
Skewness2.7528467
Sum5527135
Variance5468750.2
MonotonicityNot monotonic
2023-12-12T22:08:38.756687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1665
 
16.7%
1 62
 
0.6%
2445 31
 
0.3%
2402 25
 
0.2%
7 9
 
0.1%
9 9
 
0.1%
46 8
 
0.1%
36 8
 
0.1%
45 7
 
0.1%
34 7
 
0.1%
Other values (1751) 2461
24.6%
(Missing) 5708
57.1%
ValueCountFrequency (%)
0 1665
16.7%
1 62
 
0.6%
2 7
 
0.1%
3 1
 
< 0.1%
4 5
 
0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 9
 
0.1%
8 6
 
0.1%
9 9
 
0.1%
ValueCountFrequency (%)
30221 1
< 0.1%
30033 1
< 0.1%
10103 1
< 0.1%
9981 1
< 0.1%
9969 1
< 0.1%
9932 1
< 0.1%
9922 1
< 0.1%
9883 1
< 0.1%
9872 1
< 0.1%
9850 1
< 0.1%

매수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct168
Distinct (%)1.8%
Missing660
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean32.949786
Minimum0
Maximum9705
Zeros462
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:08:38.920002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median21
Q347
95-th percentile91
Maximum9705
Range9705
Interquartile range (IQR)37

Descriptive statistics

Standard deviation104.38716
Coefficient of variation (CV)3.168068
Kurtosis7894.0773
Mean32.949786
Median Absolute Deviation (MAD)14
Skewness85.226013
Sum307751
Variance10896.68
MonotonicityNot monotonic
2023-12-12T22:08:39.064669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 462
 
4.6%
10 290
 
2.9%
9 286
 
2.9%
7 268
 
2.7%
18 264
 
2.6%
5 259
 
2.6%
19 246
 
2.5%
20 235
 
2.4%
8 234
 
2.3%
11 223
 
2.2%
Other values (158) 6573
65.7%
(Missing) 660
 
6.6%
ValueCountFrequency (%)
0 462
4.6%
1 24
 
0.2%
2 58
 
0.6%
3 174
 
1.7%
4 186
1.9%
5 259
2.6%
6 216
2.2%
7 268
2.7%
8 234
2.3%
9 286
2.9%
ValueCountFrequency (%)
9705 1
 
< 0.1%
260 1
 
< 0.1%
249 1
 
< 0.1%
201 1
 
< 0.1%
193 1
 
< 0.1%
190 2
< 0.1%
188 1
 
< 0.1%
184 1
 
< 0.1%
181 1
 
< 0.1%
176 3
< 0.1%

촬영방향
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2787 
2745 
1258 
1192 
<NA>
474 
Other values (28)
1544 

Length

Max length4
Median length4
Mean length3.6593
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row동서
5th row

Common Values

ValueCountFrequency (%)
2787
27.9%
2745
27.5%
1258
12.6%
1192
11.9%
<NA> 474
 
4.7%
294
 
2.9%
276
 
2.8%
동서 147
 
1.5%
143
 
1.4%
서동 141
 
1.4%
Other values (23) 543
 
5.4%

Length

2023-12-12T22:08:39.252178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4045
40.5%
3937
39.4%
na 474
 
4.7%
437
 
4.4%
396
 
4.0%
동서 277
 
2.8%
서동 273
 
2.7%
남서 27
 
0.3%
북동 26
 
0.3%
북서 23
 
0.2%
Other values (8) 85
 
0.9%

촬영각도
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct44
Distinct (%)0.6%
Missing2452
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean101.78458
Minimum-91
Maximum321
Zeros3289
Zeros (%)32.9%
Negative5
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-12T22:08:39.391522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-91
5-th percentile0
Q10
median180
Q3180
95-th percentile270
Maximum321
Range412
Interquartile range (IQR)180

Descriptive statistics

Standard deviation94.632837
Coefficient of variation (CV)0.92973649
Kurtosis-1.6061353
Mean101.78458
Median Absolute Deviation (MAD)90
Skewness0.035082061
Sum768270
Variance8955.3738
MonotonicityNot monotonic
2023-12-12T22:08:39.520530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
180 3432
34.3%
0 3289
32.9%
270 389
 
3.9%
90 349
 
3.5%
315 10
 
0.1%
230 5
 
0.1%
320 5
 
0.1%
50 4
 
< 0.1%
140 4
 
< 0.1%
16 4
 
< 0.1%
Other values (34) 57
 
0.6%
(Missing) 2452
24.5%
ValueCountFrequency (%)
-91 1
 
< 0.1%
-90 2
 
< 0.1%
-14 2
 
< 0.1%
0 3289
32.9%
14 2
 
< 0.1%
16 4
 
< 0.1%
35 3
 
< 0.1%
40 1
 
< 0.1%
43 1
 
< 0.1%
45 3
 
< 0.1%
ValueCountFrequency (%)
321 2
 
< 0.1%
320 5
 
0.1%
315 10
 
0.1%
310 1
 
< 0.1%
290 2
 
< 0.1%
270 389
3.9%
263 1
 
< 0.1%
240 2
 
< 0.1%
235 2
 
< 0.1%
233 1
 
< 0.1%

최소위성수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.1%
Missing3475
Missing (%)34.8%
Infinite0
Infinite (%)0.0%
Mean6.3612261
Minimum0
Maximum9
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:08:39.633990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14
median6
Q38
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9866041
Coefficient of variation (CV)0.31229892
Kurtosis-1.4205144
Mean6.3612261
Median Absolute Deviation (MAD)2
Skewness0.036400946
Sum41507
Variance3.9465957
MonotonicityNot monotonic
2023-12-12T22:08:39.739677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 1902
19.0%
9 1554
15.5%
7 1017
 
10.2%
5 832
 
8.3%
8 690
 
6.9%
6 519
 
5.2%
0 11
 
0.1%
(Missing) 3475
34.8%
ValueCountFrequency (%)
0 11
 
0.1%
4 1902
19.0%
5 832
8.3%
6 519
 
5.2%
7 1017
10.2%
8 690
 
6.9%
9 1554
15.5%
ValueCountFrequency (%)
9 1554
15.5%
8 690
 
6.9%
7 1017
10.2%
6 519
 
5.2%
5 832
8.3%
4 1902
19.0%
0 11
 
0.1%

진행방향중복도_최대
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct15
Distinct (%)0.2%
Missing2706
Missing (%)27.1%
Infinite0
Infinite (%)0.0%
Mean46.470524
Minimum0
Maximum85
Zeros484
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:08:39.842492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median70
Q370
95-th percentile75
Maximum85
Range85
Interquartile range (IQR)60

Descriptive statistics

Standard deviation31.106671
Coefficient of variation (CV)0.669385
Kurtosis-1.7510053
Mean46.470524
Median Absolute Deviation (MAD)5
Skewness-0.40820385
Sum338956
Variance967.62501
MonotonicityNot monotonic
2023-12-12T22:08:39.953727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
10 2380
23.8%
70 2279
22.8%
75 1301
13.0%
0 484
 
4.8%
63 240
 
2.4%
80 239
 
2.4%
60 151
 
1.5%
65 95
 
0.9%
85 55
 
0.5%
73 18
 
0.2%
Other values (5) 52
 
0.5%
(Missing) 2706
27.1%
ValueCountFrequency (%)
0 484
 
4.8%
10 2380
23.8%
15 18
 
0.2%
60 151
 
1.5%
61 8
 
0.1%
62 3
 
< 0.1%
63 240
 
2.4%
64 14
 
0.1%
65 95
 
0.9%
70 2279
22.8%
ValueCountFrequency (%)
85 55
 
0.5%
83 9
 
0.1%
80 239
 
2.4%
75 1301
13.0%
73 18
 
0.2%
70 2279
22.8%
65 95
 
0.9%
64 14
 
0.1%
63 240
 
2.4%
62 3
 
< 0.1%

진행방향중복도_최소
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)0.2%
Missing2736
Missing (%)27.4%
Infinite0
Infinite (%)0.0%
Mean42.541713
Minimum0
Maximum80
Zeros484
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:08:40.072190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median60
Q365
95-th percentile70
Maximum80
Range80
Interquartile range (IQR)55

Descriptive statistics

Standard deviation28.130876
Coefficient of variation (CV)0.66125396
Kurtosis-1.7404722
Mean42.541713
Median Absolute Deviation (MAD)10
Skewness-0.38662085
Sum309023
Variance791.34619
MonotonicityNot monotonic
2023-12-12T22:08:40.192404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
10 2395
23.9%
65 1540
15.4%
70 1427
14.3%
60 763
 
7.6%
0 484
 
4.8%
61 244
 
2.4%
55 178
 
1.8%
50 98
 
1.0%
75 64
 
0.6%
80 38
 
0.4%
Other values (7) 33
 
0.3%
(Missing) 2736
27.4%
ValueCountFrequency (%)
0 484
 
4.8%
10 2395
23.9%
15 3
 
< 0.1%
50 98
 
1.0%
54 2
 
< 0.1%
55 178
 
1.8%
57 1
 
< 0.1%
58 3
 
< 0.1%
59 1
 
< 0.1%
60 763
 
7.6%
ValueCountFrequency (%)
80 38
 
0.4%
75 64
 
0.6%
70 1427
14.3%
65 1540
15.4%
63 20
 
0.2%
62 3
 
< 0.1%
61 244
 
2.4%
60 763
7.6%
59 1
 
< 0.1%
58 3
 
< 0.1%

촬영중복도_최대
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)0.3%
Missing2725
Missing (%)27.3%
Infinite0
Infinite (%)0.0%
Mean45.589553
Minimum0
Maximum75
Zeros484
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:08:40.309112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140
median45
Q360
95-th percentile70
Maximum75
Range75
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.845046
Coefficient of variation (CV)0.34755871
Kurtosis2.099246
Mean45.589553
Median Absolute Deviation (MAD)5
Skewness-1.2245376
Sum331664
Variance251.06549
MonotonicityNot monotonic
2023-12-12T22:08:40.413363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
40 2126
21.3%
60 1998
20.0%
45 1335
13.4%
0 484
 
4.8%
70 363
 
3.6%
50 357
 
3.6%
33 239
 
2.4%
30 115
 
1.1%
34 91
 
0.9%
55 49
 
0.5%
Other values (10) 118
 
1.2%
(Missing) 2725
27.3%
ValueCountFrequency (%)
0 484
 
4.8%
30 115
 
1.1%
31 4
 
< 0.1%
32 7
 
0.1%
33 239
 
2.4%
34 91
 
0.9%
35 22
 
0.2%
36 4
 
< 0.1%
40 2126
21.3%
43 11
 
0.1%
ValueCountFrequency (%)
75 7
 
0.1%
70 363
 
3.6%
65 48
 
0.5%
63 9
 
0.1%
61 4
 
< 0.1%
60 1998
20.0%
55 49
 
0.5%
50 357
 
3.6%
46 2
 
< 0.1%
45 1335
13.4%

촬영중복도_최소
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct22
Distinct (%)0.3%
Missing2726
Missing (%)27.3%
Infinite0
Infinite (%)0.0%
Mean37.005224
Minimum0
Maximum65
Zeros484
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:08:40.518612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median40
Q350
95-th percentile50
Maximum65
Range65
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.845108
Coefficient of variation (CV)0.34711608
Kurtosis2.2786327
Mean37.005224
Median Absolute Deviation (MAD)10
Skewness-1.4052128
Sum269176
Variance164.99681
MonotonicityNot monotonic
2023-12-12T22:08:40.622959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
40 1883
18.8%
50 1847
18.5%
35 1388
13.9%
30 999
 
10.0%
0 484
 
4.8%
31 246
 
2.5%
10 99
 
1.0%
45 86
 
0.9%
27 85
 
0.9%
60 56
 
0.6%
Other values (12) 101
 
1.0%
(Missing) 2726
27.3%
ValueCountFrequency (%)
0 484
4.8%
10 99
 
1.0%
15 1
 
< 0.1%
24 1
 
< 0.1%
25 15
 
0.1%
27 85
 
0.9%
28 3
 
< 0.1%
30 999
10.0%
31 246
 
2.5%
32 4
 
< 0.1%
ValueCountFrequency (%)
65 7
 
0.1%
61 4
 
< 0.1%
60 56
 
0.6%
55 48
 
0.5%
53 9
 
0.1%
50 1847
18.5%
46 2
 
< 0.1%
45 86
 
0.9%
43 5
 
0.1%
40 1883
18.8%

Interactions

2023-12-12T22:08:32.747408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:24.947618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:25.825063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:26.818881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:27.872424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:28.938223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:29.828220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:30.584789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:31.745052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:32.857535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:25.034857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:25.928804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:26.946041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:27.978636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:29.036430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:29.903913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:30.938219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:31.851478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:32.951091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:25.126138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:26.034957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:27.072940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:28.093286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:29.121260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:29.988937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:31.029055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:31.943929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:33.050398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:25.211519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:26.132087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:27.168046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:28.211026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:29.236372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:30.081764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:31.133069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:32.055262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:33.175330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:25.297383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:26.247911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:27.290888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:28.321734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:29.330127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:30.176819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:31.225471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:32.143540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:33.286355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:25.384141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:26.353832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:27.392438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:28.464381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:29.434121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:30.255045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:31.313500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:32.231696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:33.374541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:25.473887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:26.467158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:27.517929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:28.573345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:29.529723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:30.330915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:31.413014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:32.341148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:33.487107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:25.604016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:26.577592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:27.637413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:28.688555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:29.654585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:30.418225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:31.525738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:32.482382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:33.613381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:25.712179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:26.689953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:27.765286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:28.812015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:29.745829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:30.503866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:31.646102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:32.619802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:08:40.723177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
코스일련번호필터노출조리개대표필름번호매수촬영방향촬영각도최소위성수진행방향중복도_최대진행방향중복도_최소촬영중복도_최대촬영중복도_최소
코스일련번호1.0000.2970.3470.4230.1080.0000.3000.1540.2710.3740.2740.3290.382
필터0.2971.0000.9260.7710.7390.1740.7230.4860.3980.6580.4060.5920.576
노출0.3470.9261.0000.9410.6380.1520.5500.3250.7510.5530.5210.7170.759
조리개0.4230.7710.9411.0000.5910.0270.5610.3610.7740.8360.6940.8540.686
대표필름번호0.1080.7390.6380.5911.0000.1070.4000.0920.4320.1990.3520.2170.691
매수0.0000.1740.1520.0270.1071.0000.000NaNNaNNaNNaNNaNNaN
촬영방향0.3000.7230.5500.5610.4000.0001.0000.9350.4190.5450.6280.5310.622
촬영각도0.1540.4860.3250.3610.092NaN0.9351.0000.1130.3880.2830.2720.370
최소위성수0.2710.3980.7510.7740.432NaN0.4190.1131.0000.5640.5150.7750.521
진행방향중복도_최대0.3740.6580.5530.8360.199NaN0.5450.3880.5641.0000.8870.9400.969
진행방향중복도_최소0.2740.4060.5210.6940.352NaN0.6280.2830.5150.8871.0000.8780.931
촬영중복도_최대0.3290.5920.7170.8540.217NaN0.5310.2720.7750.9400.8781.0000.900
촬영중복도_최소0.3820.5760.7590.6860.691NaN0.6220.3700.5210.9690.9310.9001.000
2023-12-12T22:08:40.851606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조리개촬영방향노출필터
조리개1.0000.1850.5570.322
촬영방향0.1851.0000.1420.248
노출0.5570.1421.0000.487
필터0.3220.2480.4871.000
2023-12-12T22:08:40.941401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
코스일련번호대표필름번호매수촬영각도최소위성수진행방향중복도_최대진행방향중복도_최소촬영중복도_최대촬영중복도_최소필터노출조리개촬영방향
코스일련번호1.000-0.1350.053-0.0550.0480.1370.185-0.0140.0160.1120.1260.1700.110
대표필름번호-0.1351.0000.0890.0490.7930.1740.1260.2910.2230.4740.3590.3640.184
매수0.0530.0891.0000.005-0.0120.3940.397-0.302-0.3440.1510.1280.0210.000
촬영각도-0.0550.0490.0051.0000.060-0.029-0.036-0.064-0.0610.1740.1270.1540.706
최소위성수0.0480.793-0.0120.0601.0000.026-0.0800.0560.1560.2670.3490.5510.195
진행방향중복도_최대0.1370.1740.394-0.0290.0261.0000.906-0.182-0.1480.4590.3270.6670.296
진행방향중복도_최소0.1850.1260.397-0.036-0.0800.9061.000-0.266-0.0520.2490.2870.4680.305
촬영중복도_최대-0.0140.291-0.302-0.0640.056-0.182-0.2661.0000.7980.3390.3500.5880.286
촬영중복도_최소0.0160.223-0.344-0.0610.156-0.148-0.0520.7981.0000.2170.4330.3800.282
필터0.1120.4740.1510.1740.2670.4590.2490.3390.2171.0000.4870.3220.248
노출0.1260.3590.1280.1270.3490.3270.2870.3500.4330.4871.0000.5570.142
조리개0.1700.3640.0210.1540.5510.6670.4680.5880.3800.3220.5571.0000.185
촬영방향0.1100.1840.0000.7060.1950.2960.3050.2860.2820.2480.1420.1851.000

Missing values

2023-12-12T22:08:33.782580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:08:34.022137image/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.
2023-12-12T22:08:34.237153image/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

사업지구코드촬영일자렌즈번호코스일련번호코스번호개시시간필터노출조리개대표필름번호매수촬영방향촬영각도최소위성수진행방향중복도_최대진행방향중복도_최소촬영중복도_최대촬영중복도_최소
8231201704A208N2017-09-30152441211211118UV1/50011<NA>680775704540
20971196600A00011966-11-03544D1246<NA><NA><NA>369235<NA><NA><NA><NA><NA><NA>
16822200804A00172008-04-241308411141259Yellowauto<NA>26417<NA><NA><NA><NA><NA><NA>
25451201309A303C2013-10-3013119A591012<NA>autoauto00동서180410106050
8777201704A511N2017-06-18UC-SXp-1-91514228551116UVAUTO<NA><NA>480570704040
60363202102A101C2021-05-09DMCII230-018197197934.0<NA>AUTOAUTO<NA>850410107040
34228200711A02042008-03-0130203286NaN<NA><NA><NA><NA>65<NA><NA><NA><NA><NA><NA>
1159201405A102C2014-05-012500381421421206autoauto<NA><NA>880670604030
56378200711A02032007-11-03132301211NaN<NA><NA><NA><NA>41270<NA><NA><NA><NA><NA>
57900202102A101D2021-03-08DMCII230-01881811159.0<NA>AUTOAUTO<NA>830410107040
사업지구코드촬영일자렌즈번호코스일련번호코스번호개시시간필터노출조리개대표필름번호매수촬영방향촬영각도최소위성수진행방향중복도_최대진행방향중복도_최소촬영중복도_최대촬영중복도_최소
66958201911A03022019-12-161524467671027.0UV1/500UV05990475654535
53245201804A03012018-04-12UC-SXp-1-000152441919NaN<NA><NA><NA><NA>12270<NA>75654535
68283200611A03022006-12-2918264ANaN<NA><NA><NA>050<NA>0000
20496200300A00222003-03-2913230111140YELLOW13505.668388<NA><NA><NA><NA><NA><NA><NA>
10387201804A206N2018-06-011133204B0041045<NA>autoauto000410106050
24324200908A00362009-09-203011744N0451232yellowauto<NA>64023<NA><NA><NA><NA><NA><NA>
72712202103A454D2021-09-122756613221206.0autoauto<NA><NA>57180970654035
32159201009A103C2010-09-18001-10233351155autoauto<NA><NA>520783756353
44759196902A05561969-02-090110.0<NA><NA><NA><NA>133서동<NA><NA>60503010
77080202103A506C2021-04-05DMC01-1022062061052.0<NA>AUTO<NA><NA>95180563613331