Overview

Dataset statistics

Number of variables15
Number of observations431
Missing cells8
Missing cells (%)0.1%
Duplicate rows1
Duplicate rows (%)0.2%
Total size in memory51.9 KiB
Average record size in memory123.3 B

Variable types

Categorical11
Numeric2
Text2

Dataset

Description전국에서 광물자원탐사를 통해 산출되는 시추암추 등 국가기증에 동의한 지질·광물정보의 통합보관을 추진 중에 있으며, 2029년까지 총 479.5km의 시추,암추 보관을 목표로 하고 있습니다. 시추공번,시추공좌표,시공 심도,시공 규격등 정보를 제공하고있습니다. 더 자세한 정보는 https://komir.kmrgis.or.kr/kmrgis/portal/index.do 확인하실 수 있습니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15117592/fileData.do

Alerts

Dataset has 1 (0.2%) duplicate rowsDuplicates
좌표(X) is highly overall correlated with 광종 and 1 other fieldsHigh correlation
광종 is highly overall correlated with 입고연도 and 9 other fieldsHigh correlation
사업명 is highly overall correlated with 시공연도 and 7 other fieldsHigh correlation
소재지 is highly overall correlated with 입고연도 and 9 other fieldsHigh correlation
구분 is highly overall correlated with 심도 and 5 other fieldsHigh correlation
분류 is highly overall correlated with 시공연도 and 7 other fieldsHigh correlation
시공연도 is highly overall correlated with 입고연도 and 4 other fieldsHigh correlation
심도 is highly overall correlated with 구분 and 3 other fieldsHigh correlation
입고연도 is highly overall correlated with 시공연도 and 3 other fieldsHigh correlation
좌표기준 is highly overall correlated with 시공연도 and 7 other fieldsHigh correlation
좌표(Y) is highly overall correlated with 분류 and 4 other fieldsHigh correlation
규격 is highly overall correlated with 시공연도 and 8 other fieldsHigh correlation
보관형태 is highly overall correlated with 광종 and 2 other fieldsHigh correlation
구분 is highly imbalanced (71.5%)Imbalance
보관형태 is highly imbalanced (64.7%)Imbalance

Reproduction

Analysis started2024-04-21 12:03:46.474252
Analysis finished2024-04-21 12:03:50.551173
Duration4.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

입고연도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2020
243 
2021
186 
<NA>
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2020 243
56.4%
2021 186
43.2%
<NA> 2
 
0.5%

Length

2024-04-21T21:03:50.654822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:03:50.831551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 243
56.4%
2021 186
43.2%
na 2
 
0.5%

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
국고보조
380 
기타
48 
<NA>
 
2
센터자체
 
1

Length

Max length4
Median length4
Mean length3.7772622
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
국고보조 380
88.2%
기타 48
 
11.1%
<NA> 2
 
0.5%
센터자체 1
 
0.2%

Length

2024-04-21T21:03:51.035515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:03:51.239038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국고보조 380
88.2%
기타 48
 
11.1%
na 2
 
0.5%
센터자체 1
 
0.2%

사업명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
탐광시추
272 
민간시추
108 
광업공단
48 
<NA>
 
2
정밀조사
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
탐광시추 272
63.1%
민간시추 108
 
25.1%
광업공단 48
 
11.1%
<NA> 2
 
0.5%
정밀조사 1
 
0.2%

Length

2024-04-21T21:03:51.427507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:03:51.615309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
탐광시추 272
63.1%
민간시추 108
 
25.1%
광업공단 48
 
11.1%
na 2
 
0.5%
정밀조사 1
 
0.2%

시공연도
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)2.1%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2018.2354
Minimum2012
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-04-21T21:03:51.800361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12015
median2020
Q32021
95-th percentile2021
Maximum2021
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4374319
Coefficient of variation (CV)0.0017031868
Kurtosis-0.80026471
Mean2018.2354
Median Absolute Deviation (MAD)1
Skewness-0.98911571
Sum865823
Variance11.815938
MonotonicityNot monotonic
2024-04-21T21:03:51.982172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2020 151
35.0%
2021 131
30.4%
2012 58
 
13.5%
2013 43
 
10.0%
2019 13
 
3.0%
2018 12
 
2.8%
2017 11
 
2.6%
2015 7
 
1.6%
2014 3
 
0.7%
(Missing) 2
 
0.5%
ValueCountFrequency (%)
2012 58
 
13.5%
2013 43
 
10.0%
2014 3
 
0.7%
2015 7
 
1.6%
2017 11
 
2.6%
2018 12
 
2.8%
2019 13
 
3.0%
2020 151
35.0%
2021 131
30.4%
ValueCountFrequency (%)
2021 131
30.4%
2020 151
35.0%
2019 13
 
3.0%
2018 12
 
2.8%
2017 11
 
2.6%
2015 7
 
1.6%
2014 3
 
0.7%
2013 43
 
10.0%
2012 58
 
13.5%

분류
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
비금속
222 
금속
207 
<NA>
 
2

Length

Max length4
Median length3
Mean length2.5243619
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row금속
2nd row금속
3rd row금속
4th row금속
5th row비금속

Common Values

ValueCountFrequency (%)
비금속 222
51.5%
금속 207
48.0%
<NA> 2
 
0.5%

Length

2024-04-21T21:03:52.199404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:03:52.618878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비금속 222
51.5%
금속 207
48.0%
na 2
 
0.5%

광산명
Text

Distinct55
Distinct (%)12.8%
Missing2
Missing (%)0.5%
Memory size3.5 KiB
2024-04-21T21:03:53.195720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.9230769
Min length2

Characters and Unicode

Total characters1254
Distinct characters90
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

Unique2 ?
Unique (%)0.5%

Sample

1st row창대
2nd row창대
3rd row창대
4th row쌍전
5th row청림영월
ValueCountFrequency (%)
금성 111
25.2%
태경영천 18
 
4.1%
가사도 17
 
3.9%
삼표제이 16
 
3.6%
완도 13
 
3.0%
관인 13
 
3.0%
문명 12
 
2.7%
충무정선 11
 
2.5%
신예미 10
 
2.3%
삼표제일 10
 
2.3%
Other values (47) 209
47.5%
2024-04-21T21:03:54.020619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
성 151
 
12.0%
금 121
 
9.6%
삼 63
 
5.0%
영 38
 
3.0%
도 36
 
2.9%
제 34
 
2.7%
산 31
 
2.5%
표 31
 
2.5%
대 31
 
2.5%
태 31
 
2.5%
Other values (80) 687
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1213
96.7%
Close Punctuation 15
 
1.2%
Open Punctuation 15
 
1.2%
Space Separator 11
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 151
 
12.4%
금 121
 
10.0%
삼 63
 
5.2%
영 38
 
3.1%
도 36
 
3.0%
제 34
 
2.8%
산 31
 
2.6%
표 31
 
2.6%
대 31
 
2.6%
태 31
 
2.6%
Other values (77) 646
53.3%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1213
96.7%
Common 41
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 151
 
12.4%
금 121
 
10.0%
삼 63
 
5.2%
영 38
 
3.1%
도 36
 
3.0%
제 34
 
2.8%
산 31
 
2.6%
표 31
 
2.6%
대 31
 
2.6%
태 31
 
2.6%
Other values (77) 646
53.3%
Common
ValueCountFrequency (%)
) 15
36.6%
( 15
36.6%
11
26.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1213
96.7%
ASCII 41
 
3.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
성 151
 
12.4%
금 121
 
10.0%
삼 63
 
5.2%
영 38
 
3.1%
도 36
 
3.0%
제 34
 
2.8%
산 31
 
2.6%
표 31
 
2.6%
대 31
 
2.6%
태 31
 
2.6%
Other values (77) 646
53.3%
ASCII
ValueCountFrequency (%)
) 15
36.6%
( 15
36.6%
11
26.8%

광종
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
석회석
173 
몰리브덴
115 
금,은
45 
철
 
17
납석
 
16
Other values (11)
65 

Length

Max length14
Median length3
Mean length3.1856148
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row몰리브덴
2nd row몰리브덴
3rd row몰리브덴
4th row텅스텐
5th row석회석

Common Values

ValueCountFrequency (%)
석회석 173
40.1%
몰리브덴 115
26.7%
금,은 45
 
10.4%
철 17
 
3.9%
납석 16
 
3.7%
백운석 14
 
3.2%
티탄철 13
 
3.0%
중석 12
 
2.8%
운모 5
 
1.2%
규석 5
 
1.2%
Other values (6) 16
 
3.7%

Length

2024-04-21T21:03:54.255918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
석회석 173
38.7%
몰리브덴 115
25.7%
금,은 45
 
10.1%
철 17
 
3.8%
납석 16
 
3.6%
백운석 14
 
3.1%
티탄철 13
 
2.9%
중석 12
 
2.7%
운모 5
 
1.1%
규석 5
 
1.1%
Other values (10) 32
 
7.2%

소재지
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
충북 제천 **
122 
강원 삼척 **
52 
충북 단양 **
37 
강원 정선 **
26 
전남 진도 **
 
17
Other values (29)
177 

Length

Max length9
Median length8
Mean length7.9559165
Min length4

Unique

Unique2 ?
Unique (%)0.5%

Sample

1st row경북 영덕 **
2nd row경북 영덕 **
3rd row경북 영덕 **
4th row경북 울진 ***
5th row강원 영월 *

Common Values

ValueCountFrequency (%)
충북 제천 ** 122
28.3%
강원 삼척 ** 52
12.1%
충북 단양 ** 37
 
8.6%
강원 정선 ** 26
 
6.0%
전남 진도 ** 17
 
3.9%
경북 영덕 ** 16
 
3.7%
강원 영월 * 16
 
3.7%
전남 완도 ** 13
 
3.0%
충남 금산 ** 12
 
2.8%
강원 정선 * 11
 
2.6%
Other values (24) 109
25.3%

Length

2024-04-21T21:03:54.468637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
429
33.3%
충북 187
14.5%
제천 122
 
9.5%
강원 120
 
9.3%
삼척 52
 
4.0%
전남 48
 
3.7%
단양 46
 
3.6%
정선 37
 
2.9%
영월 25
 
1.9%
경북 21
 
1.6%
Other values (30) 202
15.7%

시추공
Text

Distinct184
Distinct (%)42.9%
Missing2
Missing (%)0.5%
Memory size3.5 KiB
2024-04-21T21:03:55.513798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length4
Mean length6.3030303
Min length4

Characters and Unicode

Total characters2704
Distinct characters33
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)35.2%

Sample

1st row21-1
2nd row21-2
3rd row21-3
4th row21-1
5th row21-1
ValueCountFrequency (%)
좌운 53
 
8.0%
21-1 35
 
5.3%
21-2 35
 
5.3%
사수 32
 
4.8%
20-1 29
 
4.4%
20-2 28
 
4.2%
개발1편 28
 
4.2%
20-3 26
 
3.9%
2중단 23
 
3.5%
21-3 22
 
3.3%
Other values (126) 355
53.3%
2024-04-21T21:03:57.148501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 449
16.6%
- 432
16.0%
2 429
15.9%
237
8.8%
0 179
 
6.6%
3 136
 
5.0%
운 93
 
3.4%
편 54
 
2.0%
발 54
 
2.0%
개 54
 
2.0%
Other values (23) 587
21.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1393
51.5%
Other Letter 535
 
19.8%
Dash Punctuation 432
 
16.0%
Space Separator 237
 
8.8%
Uppercase Letter 107
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
운 93
17.4%
편 54
10.1%
발 54
10.1%
개 54
10.1%
좌 53
9.9%
사 50
9.3%
수 50
9.3%
단 32
 
6.0%
중 32
 
6.0%
우 25
 
4.7%
Other values (2) 38
7.1%
Decimal Number
ValueCountFrequency (%)
1 449
32.2%
2 429
30.8%
0 179
 
12.8%
3 136
 
9.8%
4 53
 
3.8%
5 39
 
2.8%
7 33
 
2.4%
8 27
 
1.9%
6 26
 
1.9%
9 22
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
H 39
36.4%
B 27
25.2%
M 8
 
7.5%
N 8
 
7.5%
S 8
 
7.5%
C 5
 
4.7%
J 4
 
3.7%
K 4
 
3.7%
G 4
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 432
100.0%
Space Separator
ValueCountFrequency (%)
237
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2062
76.3%
Hangul 535
 
19.8%
Latin 107
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 449
21.8%
- 432
21.0%
2 429
20.8%
237
11.5%
0 179
 
8.7%
3 136
 
6.6%
4 53
 
2.6%
5 39
 
1.9%
7 33
 
1.6%
8 27
 
1.3%
Other values (2) 48
 
2.3%
Hangul
ValueCountFrequency (%)
운 93
17.4%
편 54
10.1%
발 54
10.1%
개 54
10.1%
좌 53
9.9%
사 50
9.3%
수 50
9.3%
단 32
 
6.0%
중 32
 
6.0%
우 25
 
4.7%
Other values (2) 38
7.1%
Latin
ValueCountFrequency (%)
H 39
36.4%
B 27
25.2%
M 8
 
7.5%
N 8
 
7.5%
S 8
 
7.5%
C 5
 
4.7%
J 4
 
3.7%
K 4
 
3.7%
G 4
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2169
80.2%
Hangul 535
 
19.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 449
20.7%
- 432
19.9%
2 429
19.8%
237
10.9%
0 179
 
8.3%
3 136
 
6.3%
4 53
 
2.4%
H 39
 
1.8%
5 39
 
1.8%
7 33
 
1.5%
Other values (11) 143
 
6.6%
Hangul
ValueCountFrequency (%)
운 93
17.4%
편 54
10.1%
발 54
10.1%
개 54
10.1%
좌 53
9.9%
사 50
9.3%
수 50
9.3%
단 32
 
6.0%
중 32
 
6.0%
우 25
 
4.7%
Other values (2) 38
7.1%

좌표기준
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
GRS80
266 
<NA>
106 
WGS84
36 
Bessel 1841
 
23

Length

Max length11
Median length5
Mean length5.0742459
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGRS80
2nd rowGRS80
3rd rowGRS80
4th rowGRS80
5th rowGRS80

Common Values

ValueCountFrequency (%)
GRS80 266
61.7%
<NA> 106
 
24.6%
WGS84 36
 
8.4%
Bessel 1841 23
 
5.3%

Length

2024-04-21T21:03:57.379214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:03:57.577809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
grs80 266
58.6%
na 106
 
23.3%
wgs84 36
 
7.9%
bessel 23
 
5.1%
1841 23
 
5.1%

좌표(X)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
1*****
194 
2*****
118 
<NA>
106 
3*****
 
8
4*****
 
5

Length

Max length6
Median length6
Mean length5.5081206
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2*****
2nd row2*****
3rd row2*****
4th row2*****
5th row1*****

Common Values

ValueCountFrequency (%)
1***** 194
45.0%
2***** 118
27.4%
<NA> 106
24.6%
3***** 8
 
1.9%
4***** 5
 
1.2%

Length

2024-04-21T21:03:57.799233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:03:58.014231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 194
45.0%
2 118
27.4%
na 106
24.6%
3 8
 
1.9%
4 5
 
1.2%

좌표(Y)
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
5*****
124 
<NA>
106 
4*****
85 
2*****
43 
3*****
42 
Other values (3)
31 

Length

Max length6
Median length6
Mean length5.5081206
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4*****
2nd row4*****
3rd row4*****
4th row4*****
5th row5*****

Common Values

ValueCountFrequency (%)
5***** 124
28.8%
<NA> 106
24.6%
4***** 85
19.7%
2***** 43
 
10.0%
3***** 42
 
9.7%
6***** 13
 
3.0%
1***** 11
 
2.6%
8***** 7
 
1.6%

Length

2024-04-21T21:03:58.241267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:03:58.472420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 124
28.8%
na 106
24.6%
4 85
19.7%
2 43
 
10.0%
3 42
 
9.7%
6 13
 
3.0%
1 11
 
2.6%
8 7
 
1.6%

심도
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)26.6%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean128.70606
Minimum4.3
Maximum480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-04-21T21:03:58.724714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.3
5-th percentile25
Q155
median110
Q3200
95-th percentile300
Maximum480
Range475.7
Interquartile range (IQR)145

Descriptive statistics

Standard deviation85.375507
Coefficient of variation (CV)0.66333711
Kurtosis0.19972031
Mean128.70606
Median Absolute Deviation (MAD)65
Skewness0.74355384
Sum55214.9
Variance7288.9771
MonotonicityNot monotonic
2024-04-21T21:03:58.978757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200.0 48
 
11.1%
150.0 37
 
8.6%
100.0 30
 
7.0%
120.0 20
 
4.6%
250.0 18
 
4.2%
90.0 13
 
3.0%
300.0 13
 
3.0%
30.0 13
 
3.0%
110.0 10
 
2.3%
80.0 10
 
2.3%
Other values (104) 217
50.3%
ValueCountFrequency (%)
4.3 1
 
0.2%
4.6 1
 
0.2%
6.0 1
 
0.2%
7.7 1
 
0.2%
8.5 2
0.5%
10.0 2
0.5%
14.0 1
 
0.2%
18.0 3
0.7%
19.2 1
 
0.2%
20.0 4
0.9%
ValueCountFrequency (%)
480.0 1
 
0.2%
400.0 1
 
0.2%
380.0 1
 
0.2%
350.0 4
 
0.9%
346.0 1
 
0.2%
330.0 1
 
0.2%
311.0 1
 
0.2%
300.0 13
3.0%
296.0 1
 
0.2%
294.0 1
 
0.2%

규격
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
NQ
246 
AQ
101 
NX
48 
NQ3
26 
BQ
 
7
Other values (2)
 
3

Length

Max length5
Median length2
Mean length2.0765661
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
NQ 246
57.1%
AQ 101
23.4%
NX 48
 
11.1%
NQ3 26
 
6.0%
BQ 7
 
1.6%
<NA> 2
 
0.5%
NQ,BQ 1
 
0.2%

Length

2024-04-21T21:03:59.210419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:03:59.420204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nq 246
57.1%
aq 101
23.4%
nx 48
 
11.1%
nq3 26
 
6.0%
bq 7
 
1.6%
na 2
 
0.5%
nq,bq 1
 
0.2%

보관형태
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Whole Core
379 
Slabbed Core
50 
<NA>
 
2

Length

Max length12
Median length10
Mean length10.204176
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWhole Core
2nd rowWhole Core
3rd rowWhole Core
4th rowWhole Core
5th rowWhole Core

Common Values

ValueCountFrequency (%)
Whole Core 379
87.9%
Slabbed Core 50
 
11.6%
<NA> 2
 
0.5%

Length

2024-04-21T21:03:59.651899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:03:59.850008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
core 429
49.9%
whole 379
44.1%
slabbed 50
 
5.8%
na 2
 
0.2%

Interactions

2024-04-21T21:03:48.976803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:03:48.497911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:03:49.221044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:03:48.734226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T21:03:59.996141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
입고연도구분사업명시공연도분류광산명광종소재지좌표기준좌표(X)좌표(Y)심도규격보관형태
입고연도1.0000.1330.6810.9400.3440.8300.5540.7370.2460.2690.2170.2500.7630.000
구분0.1331.0001.0000.4040.2231.0000.8131.0000.8900.4150.3600.8880.9400.067
사업명0.6811.0001.0000.8940.9190.9970.8550.9870.5840.6980.3010.8900.9110.394
시공연도0.9400.4040.8941.0000.6910.8650.7330.7920.8480.1980.6720.5980.8360.509
분류0.3440.2230.9190.6911.0001.0001.0000.9560.3320.5200.4990.8320.9240.000
광산명0.8301.0000.9970.8651.0001.0001.0001.0000.9771.0000.9860.8770.9430.923
광종0.5540.8130.8550.7331.0001.0001.0000.9910.8310.7640.8690.6240.8510.766
소재지0.7371.0000.9870.7920.9561.0000.9911.0000.9550.9960.9750.8230.9010.806
좌표기준0.2460.8900.5840.8480.3320.9770.8310.9551.0000.4560.5640.8680.7160.099
좌표(X)0.2690.4150.6980.1980.5201.0000.7640.9960.4561.0000.6380.4360.3930.119
좌표(Y)0.2170.3600.3010.6720.4990.9860.8690.9750.5640.6381.0000.4370.6900.502
심도0.2500.8880.8900.5980.8320.8770.6240.8230.8680.4360.4371.0000.6780.406
규격0.7630.9400.9110.8360.9240.9430.8510.9010.7160.3930.6900.6781.0000.596
보관형태0.0000.0670.3940.5090.0000.9230.7660.8060.0990.1190.5020.4060.5961.000
2024-04-21T21:04:00.253036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
좌표기준좌표(X)광종사업명소재지좌표(Y)구분보관형태규격입고연도분류
좌표기준1.0000.4510.5590.5950.7530.4510.5970.1640.7030.4000.531
좌표(X)0.4511.0000.5450.3410.9360.4960.4060.0780.3300.1780.353
광종0.5590.5451.0000.6740.8840.6270.5400.7090.6000.5010.985
사업명0.5950.3410.6741.0000.9080.2090.9990.2630.7950.4790.741
소재지0.7530.9360.8840.9081.0000.8310.9460.6860.6400.6180.873
좌표(Y)0.4510.4960.6270.2090.8311.0000.2580.5340.5350.2310.531
구분0.5970.4060.5400.9990.9460.2581.0000.1110.7010.2200.364
보관형태0.1640.0780.7090.2630.6860.5340.1111.0000.4310.0000.000
규격0.7030.3300.6000.7950.6400.5350.7010.4311.0000.5670.751
입고연도0.4000.1780.5010.4790.6180.2310.2200.0000.5671.0000.223
분류0.5310.3530.9850.7410.8730.5310.3640.0000.7510.2231.000
2024-04-21T21:04:00.485776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시공연도심도입고연도구분사업명분류광종소재지좌표기준좌표(X)좌표(Y)규격보관형태
시공연도1.0000.4330.8080.2940.5980.6260.4080.4300.5390.1280.4810.6620.402
심도0.4331.0000.1900.8250.7590.6580.2850.4510.5840.2910.2470.4400.309
입고연도0.8080.1901.0000.2200.4790.2230.5010.6180.4000.1780.2310.5670.000
구분0.2940.8250.2201.0000.9990.3640.5400.9460.5970.4060.2580.7010.111
사업명0.5980.7590.4790.9991.0000.7410.6740.9080.5950.3410.2090.7950.263
분류0.6260.6580.2230.3640.7411.0000.9850.8730.5310.3530.5310.7510.000
광종0.4080.2850.5010.5400.6740.9851.0000.8840.5590.5450.6270.6000.709
소재지0.4300.4510.6180.9460.9080.8730.8841.0000.7530.9360.8310.6400.686
좌표기준0.5390.5840.4000.5970.5950.5310.5590.7531.0000.4510.4510.7030.164
좌표(X)0.1280.2910.1780.4060.3410.3530.5450.9360.4511.0000.4960.3300.078
좌표(Y)0.4810.2470.2310.2580.2090.5310.6270.8310.4510.4961.0000.5350.534
규격0.6620.4400.5670.7010.7950.7510.6000.6400.7030.3300.5351.0000.431
보관형태0.4020.3090.0000.1110.2630.0000.7090.6860.1640.0780.5340.4311.000

Missing values

2024-04-21T21:03:49.570033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T21:03:49.906580image/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.
2024-04-21T21:03:50.230121image/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

입고연도구분사업명시공연도분류광산명광종소재지시추공좌표기준좌표(X)좌표(Y)심도규격보관형태
02021국고보조탐광시추2021금속창대몰리브덴경북 영덕 **21-1GRS802*****4*****90.0NQWhole Core
12021국고보조탐광시추2021금속창대몰리브덴경북 영덕 **21-2GRS802*****4*****90.0NQWhole Core
22021국고보조탐광시추2021금속창대몰리브덴경북 영덕 **21-3GRS802*****4*****80.0NQWhole Core
32021국고보조탐광시추2021금속쌍전텅스텐경북 울진 ***21-1GRS802*****4*****210.0NQWhole Core
42021국고보조탐광시추2021비금속청림영월석회석강원 영월 *21-1GRS801*****5*****150.0NQWhole Core
52021국고보조탐광시추2021비금속청림영월석회석강원 영월 *21-2GRS801*****5*****250.0NQWhole Core
62021국고보조탐광시추2021비금속청림영월석회석강원 영월 *21-3GRS801*****5*****140.0NQWhole Core
72021국고보조탐광시추2021비금속경기금산석회석충남 금산 **21-1GRS802*****3*****170.0NQWhole Core
82021국고보조탐광시추2021비금속경기금산석회석충남 금산 **21-2GRS802*****3*****150.0NQWhole Core
92021국고보조탐광시추2021비금속아세아영월석회석강원 영월 *21-1GRS801*****5*****200.0NQ3Whole Core
입고연도구분사업명시공연도분류광산명광종소재지시추공좌표기준좌표(X)좌표(Y)심도규격보관형태
4212020기타광업공단2020금속문명금,은경북 영덕 **MH-1GRS802*****4*****28.0NXWhole Core
4222020기타광업공단2020금속문명금,은경북 영덕 **MH-2GRS802*****4*****19.2NXWhole Core
4232020기타광업공단2020금속문명금,은경북 영덕 **MH-3GRS802*****4*****40.0NXWhole Core
4242020기타광업공단2020금속문명금,은경북 영덕 **MH-4GRS802*****4*****10.0NXWhole Core
4252020기타광업공단2020금속문명금,은경북 영덕 **MH-5GRS802*****4*****10.0NXWhole Core
4262020기타광업공단2020금속문명금,은경북 영덕 **MH-6GRS802*****4*****33.3NXWhole Core
4272020기타광업공단2020금속문명금,은경북 영덕 **MH-7GRS802*****4*****26.7NXWhole Core
4282020기타광업공단2020금속문명금,은경북 영덕 **MH-8GRS802*****4*****4.3NXWhole Core
429<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
430<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

입고연도구분사업명시공연도분류광산명광종소재지시추공좌표기준좌표(X)좌표(Y)심도규격보관형태# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2