Overview

Dataset statistics

Number of variables20
Number of observations59
Missing cells58
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 KiB
Average record size in memory168.2 B

Variable types

Categorical12
Text2
DateTime1
Numeric5

Dataset

Description경상북도 김천시의 보호수 현황으로 보호수지정일자, 보호수유형명, 나무종류, 그루수, 소재지지번주소 등의 정보를 제공합니다.
Author경상북도 김천시
URLhttps://www.data.go.kr/data/15090366/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
관리기관명 has constant value ""Constant
보호수해지일자 has constant value ""Constant
보호수유형명 has constant value ""Constant
품격명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
나무종류 is highly overall correlated with 과명 and 1 other fieldsHigh correlation
학명 is highly overall correlated with 과명 and 1 other fieldsHigh correlation
과명 is highly overall correlated with 학명 and 1 other fieldsHigh correlation
나무나이 is highly overall correlated with 가슴높이둘레High correlation
가슴높이둘레 is highly overall correlated with 나무나이High correlation
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
과명 is highly imbalanced (57.0%)Imbalance
학명 is highly imbalanced (57.0%)Imbalance
나무종류 is highly imbalanced (57.0%)Imbalance
그루수 is highly imbalanced (84.4%)Imbalance
보호수해지일자 has 58 (98.3%) missing valuesMissing

Reproduction

Analysis started2023-12-11 23:01:43.178216
Analysis finished2023-12-11 23:01:47.076418
Duration3.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size604.0 B
경상북도
59 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 59
100.0%

Length

2023-12-12T08:01:47.135790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:47.245213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 59
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size604.0 B
김천시
59 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
김천시 59
100.0%

Length

2023-12-12T08:01:47.350667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:47.464009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
김천시 59
100.0%

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size604.0 B
경상북도 김천시청
59 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도 김천시청
2nd row경상북도 김천시청
3rd row경상북도 김천시청
4th row경상북도 김천시청
5th row경상북도 김천시청

Common Values

ValueCountFrequency (%)
경상북도 김천시청 59
100.0%

Length

2023-12-12T08:01:47.574301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:47.750847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 59
50.0%
김천시청 59
50.0%

지정번호
Text

Distinct58
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size604.0 B
2023-12-12T08:01:47.950887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length8.7118644
Min length5

Characters and Unicode

Total characters514
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

Unique57 ?
Unique (%)96.6%

Sample

1st row2005-03-01
2nd row2019-07-30
3rd row11-26-37
4th row11-49
5th row11-26-2-3
ValueCountFrequency (%)
2011-03-02 2
 
3.4%
11-26-13-2 1
 
1.7%
11-3-16-1 1
 
1.7%
11-26-30 1
 
1.7%
11-26-22 1
 
1.7%
11-26-23 1
 
1.7%
11-26-11-13-1 1
 
1.7%
11-26-21 1
 
1.7%
2009-03-01 1
 
1.7%
11-26-26 1
 
1.7%
Other values (48) 48
81.4%
2023-12-12T08:01:48.365394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 149
29.0%
- 138
26.8%
2 77
15.0%
6 50
 
9.7%
0 40
 
7.8%
3 27
 
5.3%
5 12
 
2.3%
7 7
 
1.4%
4 6
 
1.2%
9 5
 
1.0%
Other values (2) 3
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 375
73.0%
Dash Punctuation 138
 
26.8%
Math Symbol 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 149
39.7%
2 77
20.5%
6 50
 
13.3%
0 40
 
10.7%
3 27
 
7.2%
5 12
 
3.2%
7 7
 
1.9%
4 6
 
1.6%
9 5
 
1.3%
8 2
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 138
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 514
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 149
29.0%
- 138
26.8%
2 77
15.0%
6 50
 
9.7%
0 40
 
7.8%
3 27
 
5.3%
5 12
 
2.3%
7 7
 
1.4%
4 6
 
1.2%
9 5
 
1.0%
Other values (2) 3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 514
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 149
29.0%
- 138
26.8%
2 77
15.0%
6 50
 
9.7%
0 40
 
7.8%
3 27
 
5.3%
5 12
 
2.3%
7 7
 
1.4%
4 6
 
1.2%
9 5
 
1.0%
Other values (2) 3
 
0.6%

보호수지정일자
Categorical

Distinct9
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size604.0 B
1982-10-29
42 
1982-08-27
6 
1994-10-21
 
4
2004-12-16
 
2
2005-12-15
 
1
Other values (4)
 
4

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique5 ?
Unique (%)8.5%

Sample

1st row2005-12-15
2nd row2019-07-30
3rd row1993-07-07
4th row1982-10-29
5th row1994-10-21

Common Values

ValueCountFrequency (%)
1982-10-29 42
71.2%
1982-08-27 6
 
10.2%
1994-10-21 4
 
6.8%
2004-12-16 2
 
3.4%
2005-12-15 1
 
1.7%
2019-07-30 1
 
1.7%
1993-07-07 1
 
1.7%
2009-02-19 1
 
1.7%
2006-05-22 1
 
1.7%

Length

2023-12-12T08:01:48.529292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:48.644519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1982-10-29 42
71.2%
1982-08-27 6
 
10.2%
1994-10-21 4
 
6.8%
2004-12-16 2
 
3.4%
2005-12-15 1
 
1.7%
2019-07-30 1
 
1.7%
1993-07-07 1
 
1.7%
2009-02-19 1
 
1.7%
2006-05-22 1
 
1.7%

보호수해지일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing58
Missing (%)98.3%
Memory size604.0 B
Minimum2019-04-18 00:00:00
Maximum2019-04-18 00:00:00
2023-12-12T08:01:48.742972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:48.862100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

보호수유형명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size604.0 B
노목
59 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
노목 59
100.0%

Length

2023-12-12T08:01:48.994864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:49.120649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노목 59
100.0%

과명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size604.0 B
느릅나무과(Ulmaceae)
47 
팽나무과(Celtidaceae)
 
4
버드나무과(Salicaceae)
 
3
은행나무과(Ginkgoaceae)
 
2
소나무과(Pinaceae)
 
1
Other values (2)
 
2

Length

Max length18
Median length15
Mean length15.288136
Min length13

Unique

Unique3 ?
Unique (%)5.1%

Sample

1st row느릅나무과(Ulmaceae)
2nd row버드나무과(Salicaceae)
3rd row느릅나무과(Ulmaceae)
4th row느릅나무과(Ulmaceae)
5th row느릅나무과(Ulmaceae)

Common Values

ValueCountFrequency (%)
느릅나무과(Ulmaceae) 47
79.7%
팽나무과(Celtidaceae) 4
 
6.8%
버드나무과(Salicaceae) 3
 
5.1%
은행나무과(Ginkgoaceae) 2
 
3.4%
소나무과(Pinaceae) 1
 
1.7%
장미과(Rosaceae) 1
 
1.7%
콩과(Leguminosae) 1
 
1.7%

Length

2023-12-12T08:01:49.244340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:49.368647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
느릅나무과(ulmaceae 47
79.7%
팽나무과(celtidaceae 4
 
6.8%
버드나무과(salicaceae 3
 
5.1%
은행나무과(ginkgoaceae 2
 
3.4%
소나무과(pinaceae 1
 
1.7%
장미과(rosaceae 1
 
1.7%
콩과(leguminosae 1
 
1.7%

학명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size604.0 B
Zelkova serrata
47 
Celtis sinensis Persoon
 
4
Salix chaenomeloides Kimura
 
3
Ginkgo biloba
 
2
Pinus densiflora
 
1
Other values (2)
 
2

Length

Max length27
Median length15
Mean length16.135593
Min length13

Unique

Unique3 ?
Unique (%)5.1%

Sample

1st rowZelkova serrata
2nd rowSalix chaenomeloides Kimura
3rd rowZelkova serrata
4th rowZelkova serrata
5th rowZelkova serrata

Common Values

ValueCountFrequency (%)
Zelkova serrata 47
79.7%
Celtis sinensis Persoon 4
 
6.8%
Salix chaenomeloides Kimura 3
 
5.1%
Ginkgo biloba 2
 
3.4%
Pinus densiflora 1
 
1.7%
pyrus pyrifolia 1
 
1.7%
Sophora japonicum 1
 
1.7%

Length

2023-12-12T08:01:49.495276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:49.610940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
zelkova 47
37.6%
serrata 47
37.6%
celtis 4
 
3.2%
sinensis 4
 
3.2%
persoon 4
 
3.2%
salix 3
 
2.4%
chaenomeloides 3
 
2.4%
kimura 3
 
2.4%
ginkgo 2
 
1.6%
biloba 2
 
1.6%
Other values (6) 6
 
4.8%

나무종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size604.0 B
느티
47 
팽나무
 
4
왕버들
 
3
은행
 
2
소나무
 
1
Other values (2)
 
2

Length

Max length3
Median length2
Mean length2.1186441
Min length1

Unique

Unique3 ?
Unique (%)5.1%

Sample

1st row느티
2nd row왕버들
3rd row느티
4th row느티
5th row느티

Common Values

ValueCountFrequency (%)
느티 47
79.7%
팽나무 4
 
6.8%
왕버들 3
 
5.1%
은행 2
 
3.4%
소나무 1
 
1.7%
배 1
 
1.7%
회화 1
 
1.7%

Length

2023-12-12T08:01:49.728565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:49.844049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
느티 47
79.7%
팽나무 4
 
6.8%
왕버들 3
 
5.1%
은행 2
 
3.4%
소나무 1
 
1.7%
배 1
 
1.7%
회화 1
 
1.7%

그루수
Categorical

IMBALANCE 

Distinct3
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size604.0 B
1
57 
2
 
1
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)3.4%

Sample

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

Common Values

ValueCountFrequency (%)
1 57
96.6%
2 1
 
1.7%
5 1
 
1.7%

Length

2023-12-12T08:01:49.953046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:50.052643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 57
96.6%
2 1
 
1.7%
5 1
 
1.7%

나무나이
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean330.30508
Minimum100
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-12T08:01:50.152927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile118
Q1250
median350
Q3400
95-th percentile500
Maximum600
Range500
Interquartile range (IQR)150

Descriptive statistics

Standard deviation116.30804
Coefficient of variation (CV)0.35212307
Kurtosis-0.32551596
Mean330.30508
Median Absolute Deviation (MAD)50
Skewness-0.15336947
Sum19488
Variance13527.56
MonotonicityNot monotonic
2023-12-12T08:01:50.267942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
350 9
15.3%
300 9
15.3%
450 7
11.9%
400 6
10.2%
200 5
8.5%
250 4
6.8%
500 3
 
5.1%
100 3
 
5.1%
320 3
 
5.1%
150 2
 
3.4%
Other values (7) 8
13.6%
ValueCountFrequency (%)
100 3
 
5.1%
120 1
 
1.7%
150 2
 
3.4%
160 1
 
1.7%
200 5
8.5%
250 4
6.8%
300 9
15.3%
320 3
 
5.1%
350 9
15.3%
360 1
 
1.7%
ValueCountFrequency (%)
600 1
 
1.7%
550 1
 
1.7%
500 3
 
5.1%
478 1
 
1.7%
450 7
11.9%
400 6
10.2%
380 2
 
3.4%
360 1
 
1.7%
350 9
15.3%
320 3
 
5.1%

나무높이
Real number (ℝ)

Distinct14
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.728814
Minimum10
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-12T08:01:50.382046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11
Q113
median15
Q317
95-th percentile20.1
Maximum25
Range15
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1118822
Coefficient of variation (CV)0.19784596
Kurtosis0.1691541
Mean15.728814
Median Absolute Deviation (MAD)2
Skewness0.45726212
Sum928
Variance9.6838106
MonotonicityNot monotonic
2023-12-12T08:01:50.483165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
17 10
16.9%
15 8
13.6%
13 8
13.6%
14 6
10.2%
16 5
8.5%
20 5
8.5%
19 4
 
6.8%
12 4
 
6.8%
18 2
 
3.4%
11 2
 
3.4%
Other values (4) 5
8.5%
ValueCountFrequency (%)
10 2
 
3.4%
11 2
 
3.4%
12 4
 
6.8%
13 8
13.6%
14 6
10.2%
15 8
13.6%
16 5
8.5%
17 10
16.9%
18 2
 
3.4%
19 4
 
6.8%
ValueCountFrequency (%)
25 1
 
1.7%
22 1
 
1.7%
21 1
 
1.7%
20 5
8.5%
19 4
 
6.8%
18 2
 
3.4%
17 10
16.9%
16 5
8.5%
15 8
13.6%
14 6
10.2%

가슴높이둘레
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.47458
Minimum150
Maximum946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-12T08:01:50.613080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile271.8
Q1402.5
median490
Q3598.5
95-th percentile776.8
Maximum946
Range796
Interquartile range (IQR)196

Descriptive statistics

Standard deviation153.63934
Coefficient of variation (CV)0.3069873
Kurtosis0.55058685
Mean500.47458
Median Absolute Deviation (MAD)95
Skewness0.41594697
Sum29528
Variance23605.047
MonotonicityNot monotonic
2023-12-12T08:01:50.735584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
570 3
 
5.1%
620 3
 
5.1%
520 3
 
5.1%
450 2
 
3.4%
430 2
 
3.4%
400 2
 
3.4%
486 2
 
3.4%
434 2
 
3.4%
535 1
 
1.7%
335 1
 
1.7%
Other values (38) 38
64.4%
ValueCountFrequency (%)
150 1
1.7%
234 1
1.7%
270 1
1.7%
272 1
1.7%
290 1
1.7%
310 1
1.7%
316 1
1.7%
320 1
1.7%
335 1
1.7%
340 1
1.7%
ValueCountFrequency (%)
946 1
1.7%
830 1
1.7%
820 1
1.7%
772 1
1.7%
754 1
1.7%
654 1
1.7%
653 1
1.7%
650 1
1.7%
636 1
1.7%
627 1
1.7%

품격명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size604.0 B
시·군나무
59 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row시·군나무
2nd row시·군나무
3rd row시·군나무
4th row시·군나무
5th row시·군나무

Common Values

ValueCountFrequency (%)
시·군나무 59
100.0%

Length

2023-12-12T08:01:50.858047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:51.186338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시·군나무 59
100.0%

지목명
Categorical

Distinct6
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size604.0 B
임야
24 
도로
18 
대
9 
답
4 
전
3 

Length

Max length4
Median length2
Mean length1.7627119
Min length1

Unique

Unique1 ?
Unique (%)1.7%

Sample

1st row임야
2nd row도로
3rd row임야
4th row임야
5th row임야

Common Values

ValueCountFrequency (%)
임야 24
40.7%
도로 18
30.5%
대 9
 
15.3%
답 4
 
6.8%
전 3
 
5.1%
학교용지 1
 
1.7%

Length

2023-12-12T08:01:51.307639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:51.479096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임야 24
40.7%
도로 18
30.5%
대 9
 
15.3%
답 4
 
6.8%
전 3
 
5.1%
학교용지 1
 
1.7%

소재지지번주소
Text

Distinct58
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size604.0 B
2023-12-12T08:01:51.742727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length20.474576
Min length16

Characters and Unicode

Total characters1208
Distinct characters91
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

Unique57 ?
Unique (%)96.6%

Sample

1st row경상북도 김천시 아포읍 송천리 1149
2nd row경상북도 김천시 아포읍 예리 1002-1
3rd row경상북도 김천시 농소면 월곡리 산75-2
4th row경상북도 김천시 농소면 노곡리 455
5th row경상북도 김천시 남면 월명리 903-13
ValueCountFrequency (%)
경상북도 59
20.6%
김천시 59
20.6%
구성면 7
 
2.4%
부항면 7
 
2.4%
어모면 6
 
2.1%
감문면 6
 
2.1%
증산면 4
 
1.4%
대항면 4
 
1.4%
남면 4
 
1.4%
지례면 3
 
1.0%
Other values (111) 128
44.6%
2023-12-12T08:01:52.148543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
228
18.9%
천 64
 
5.3%
도 62
 
5.1%
상 61
 
5.0%
경 59
 
4.9%
북 59
 
4.9%
김 59
 
4.9%
시 59
 
4.9%
리 51
 
4.2%
면 49
 
4.1%
Other values (81) 457
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 745
61.7%
Space Separator 228
 
18.9%
Decimal Number 208
 
17.2%
Dash Punctuation 27
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 64
 
8.6%
도 62
 
8.3%
상 61
 
8.2%
경 59
 
7.9%
북 59
 
7.9%
김 59
 
7.9%
시 59
 
7.9%
리 51
 
6.8%
면 49
 
6.6%
곡 14
 
1.9%
Other values (69) 208
27.9%
Decimal Number
ValueCountFrequency (%)
1 45
21.6%
3 25
12.0%
2 24
11.5%
9 22
10.6%
4 20
9.6%
0 19
9.1%
5 16
 
7.7%
7 15
 
7.2%
6 12
 
5.8%
8 10
 
4.8%
Space Separator
ValueCountFrequency (%)
228
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 745
61.7%
Common 463
38.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 64
 
8.6%
도 62
 
8.3%
상 61
 
8.2%
경 59
 
7.9%
북 59
 
7.9%
김 59
 
7.9%
시 59
 
7.9%
리 51
 
6.8%
면 49
 
6.6%
곡 14
 
1.9%
Other values (69) 208
27.9%
Common
ValueCountFrequency (%)
228
49.2%
1 45
 
9.7%
- 27
 
5.8%
3 25
 
5.4%
2 24
 
5.2%
9 22
 
4.8%
4 20
 
4.3%
0 19
 
4.1%
5 16
 
3.5%
7 15
 
3.2%
Other values (2) 22
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 745
61.7%
ASCII 463
38.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
228
49.2%
1 45
 
9.7%
- 27
 
5.8%
3 25
 
5.4%
2 24
 
5.2%
9 22
 
4.8%
4 20
 
4.3%
0 19
 
4.1%
5 16
 
3.5%
7 15
 
3.2%
Other values (2) 22
 
4.8%
Hangul
ValueCountFrequency (%)
천 64
 
8.6%
도 62
 
8.3%
상 61
 
8.2%
경 59
 
7.9%
북 59
 
7.9%
김 59
 
7.9%
시 59
 
7.9%
리 51
 
6.8%
면 49
 
6.6%
곡 14
 
1.9%
Other values (69) 208
27.9%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.084122
Minimum35.867438
Maximum36.235458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-12T08:01:52.276901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.867438
5-th percentile35.917111
Q136.01308
median36.092863
Q336.155924
95-th percentile36.220716
Maximum36.235458
Range0.3680205
Interquartile range (IQR)0.142844

Descriptive statistics

Standard deviation0.094174502
Coefficient of variation (CV)0.0026098599
Kurtosis-0.7974045
Mean36.084122
Median Absolute Deviation (MAD)0.077216
Skewness-0.25202305
Sum2128.9632
Variance0.0088688369
MonotonicityNot monotonic
2023-12-12T08:01:52.402089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.155924 2
 
3.4%
36.1511875 1
 
1.7%
35.986509 1
 
1.7%
36.020607 1
 
1.7%
36.004519 1
 
1.7%
36.044172 1
 
1.7%
36.0163125 1
 
1.7%
35.980172 1
 
1.7%
35.980392 1
 
1.7%
35.963472 1
 
1.7%
Other values (48) 48
81.4%
ValueCountFrequency (%)
35.8674375 1
1.7%
35.911539 1
1.7%
35.912228 1
1.7%
35.917654 1
1.7%
35.934944 1
1.7%
35.963472 1
1.7%
35.975301 1
1.7%
35.980172 1
1.7%
35.980392 1
1.7%
35.982106 1
1.7%
ValueCountFrequency (%)
36.235458 1
1.7%
36.2280625 1
1.7%
36.2274375 1
1.7%
36.219969 1
1.7%
36.218509 1
1.7%
36.213303 1
1.7%
36.207853 1
1.7%
36.19925202 1
1.7%
36.1934375 1
1.7%
36.19176 1
1.7%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.08228
Minimum127.92117
Maximum128.27219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-12T08:01:52.528968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.92117
5-th percentile127.94093
Q1128.02078
median128.07665
Q3128.14296
95-th percentile128.24129
Maximum128.27219
Range0.3510165
Interquartile range (IQR)0.122184

Descriptive statistics

Standard deviation0.092369144
Coefficient of variation (CV)0.00072117037
Kurtosis-0.67588715
Mean128.08228
Median Absolute Deviation (MAD)0.058969
Skewness0.35384494
Sum7556.8544
Variance0.0085320588
MonotonicityNot monotonic
2023-12-12T08:01:52.664004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.088793 2
 
3.4%
128.2721875 1
 
1.7%
127.958464 1
 
1.7%
128.048892 1
 
1.7%
128.030351 1
 
1.7%
128.076651 1
 
1.7%
127.9873125 1
 
1.7%
128.015917 1
 
1.7%
128.015253 1
 
1.7%
128.037049 1
 
1.7%
Other values (48) 48
81.4%
ValueCountFrequency (%)
127.921171 1
1.7%
127.93071 1
1.7%
127.939849 1
1.7%
127.941046 1
1.7%
127.955896 1
1.7%
127.958464 1
1.7%
127.971704 1
1.7%
127.976963 1
1.7%
127.980141 1
1.7%
127.9873125 1
1.7%
ValueCountFrequency (%)
128.2721875 1
1.7%
128.266096 1
1.7%
128.2595625 1
1.7%
128.239258 1
1.7%
128.236249 1
1.7%
128.218052 1
1.7%
128.21798 1
1.7%
128.204166 1
1.7%
128.204043 1
1.7%
128.202699 1
1.7%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size604.0 B
2023-09-07
59 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-07
2nd row2023-09-07
3rd row2023-09-07
4th row2023-09-07
5th row2023-09-07

Common Values

ValueCountFrequency (%)
2023-09-07 59
100.0%

Length

2023-12-12T08:01:52.799303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:52.885780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-07 59
100.0%

Interactions

2023-12-12T08:01:46.157324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:43.958184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:44.626324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:45.120157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:45.650322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:46.257305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:44.035970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:44.720490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:45.218749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:45.751653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:46.370717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:44.135425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:44.822898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:45.316233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:45.871825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:46.478995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:44.210728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:44.935510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:45.453762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:45.959697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:46.567572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:44.527485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:45.027685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:45.557917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:46.056435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:01:52.967258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정번호보호수지정일자과명학명나무종류그루수나무나이나무높이가슴높이둘레지목명소재지지번주소위도경도
지정번호1.0001.0000.0000.0000.0001.0001.0001.0001.0000.9290.9970.9640.946
보호수지정일자1.0001.0000.0000.0000.0000.0000.4150.5850.2680.0001.0000.6690.596
과명0.0000.0001.0001.0001.0000.0000.0000.2510.0000.0000.8710.0000.000
학명0.0000.0001.0001.0001.0000.0000.0000.2510.0000.0000.8710.0000.000
나무종류0.0000.0001.0001.0001.0000.0000.0000.2510.0000.0000.8710.0000.000
그루수1.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.0000.000
나무나이1.0000.4150.0000.0000.0000.0001.0000.0960.5520.0001.0000.5720.000
나무높이1.0000.5850.2510.2510.2510.0000.0961.0000.0000.2811.0000.4300.000
가슴높이둘레1.0000.2680.0000.0000.0000.0000.5520.0001.0000.0001.0000.2920.000
지목명0.9290.0000.0000.0000.0000.0000.0000.2810.0001.0001.0000.2650.294
소재지지번주소0.9971.0000.8710.8710.8711.0001.0001.0001.0001.0001.0001.0001.000
위도0.9640.6690.0000.0000.0000.0000.5720.4300.2920.2651.0001.0000.501
경도0.9460.5960.0000.0000.0000.0000.0000.0000.0000.2941.0000.5011.000
2023-12-12T08:01:53.096649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
나무종류학명과명지목명그루수보호수지정일자
나무종류1.0001.0001.0000.0000.0000.000
학명1.0001.0001.0000.0000.0000.000
과명1.0001.0001.0000.0000.0000.000
지목명0.0000.0000.0001.0000.0000.000
그루수0.0000.0000.0000.0001.0000.000
보호수지정일자0.0000.0000.0000.0000.0001.000
2023-12-12T08:01:53.200227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
나무나이나무높이가슴높이둘레위도경도보호수지정일자과명학명나무종류그루수지목명
나무나이1.000-0.1210.512-0.253-0.1600.1930.0000.0000.0000.0000.000
나무높이-0.1211.0000.0520.2780.1070.0520.2110.2110.2110.0000.000
가슴높이둘레0.5120.0521.000-0.225-0.0730.0000.3590.3590.3590.0000.000
위도-0.2530.278-0.2251.0000.6530.3760.0000.0000.0000.0000.126
경도-0.1600.107-0.0730.6531.0000.3140.0000.0000.0000.0000.144
보호수지정일자0.1930.0520.0000.3760.3141.0000.0000.0000.0000.0000.000
과명0.0000.2110.3590.0000.0000.0001.0001.0001.0000.0000.000
학명0.0000.2110.3590.0000.0000.0001.0001.0001.0000.0000.000
나무종류0.0000.2110.3590.0000.0000.0001.0001.0001.0000.0000.000
그루수0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
지목명0.0000.0000.0000.1260.1440.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T08:01:46.707460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:01:46.986446image/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.

Sample

시도명시군구명관리기관명지정번호보호수지정일자보호수해지일자보호수유형명과명학명나무종류그루수나무나이나무높이가슴높이둘레품격명지목명소재지지번주소위도경도데이터기준일자
0경상북도김천시경상북도 김천시청2005-03-012005-12-15<NA>노목느릅나무과(Ulmaceae)Zelkova serrata느티135018450시·군나무임야경상북도 김천시 아포읍 송천리 114936.151187128.2721882023-09-07
1경상북도김천시경상북도 김천시청2019-07-302019-07-30<NA>노목버드나무과(Salicaceae)Salix chaenomeloides Kimura왕버들125019597시·군나무도로경상북도 김천시 아포읍 예리 1002-136.193438128.2595622023-09-07
2경상북도김천시경상북도 김천시청11-26-371993-07-07<NA>노목느릅나무과(Ulmaceae)Zelkova serrata느티135015430시·군나무임야경상북도 김천시 농소면 월곡리 산75-236.101695128.1679842023-09-07
3경상북도김천시경상북도 김천시청11-491982-10-29<NA>노목느릅나무과(Ulmaceae)Zelkova serrata느티160014570시·군나무임야경상북도 김천시 농소면 노곡리 45536.073426128.2041662023-09-07
4경상북도김천시경상북도 김천시청11-26-2-31994-10-21<NA>노목느릅나무과(Ulmaceae)Zelkova serrata느티145012620시·군나무임야경상북도 김천시 남면 월명리 903-1336.049671128.2392582023-09-07
5경상북도김천시경상북도 김천시청11-26-2-11982-10-29<NA>노목느릅나무과(Ulmaceae)Zelkova serrata느티120013460시·군나무대경상북도 김천시 남면 운곡리 379-136.092863128.2180522023-09-07
6경상북도김천시경상북도 김천시청11-26-21982-10-29<NA>노목느릅나무과(Ulmaceae)Zelkova serrata느티140013570시·군나무임야경상북도 김천시 남면 오봉리 19136.086216128.2660962023-09-07
7경상북도김천시경상북도 김천시청11-26-2-21982-10-29<NA>노목느릅나무과(Ulmaceae)Zelkova serrata느티125022400시·군나무전경상북도 김천시 남면 송곡리 38436.083979128.2362492023-09-07
8경상북도김천시경상북도 김천시청11-26-5-11982-10-29<NA>노목느릅나무과(Ulmaceae)Zelkova serrata느티120017486시·군나무도로경상북도 김천시 감문면 성촌리 19-136.199252128.217982023-09-07
9경상북도김천시경상북도 김천시청11-26-5-7-11982-10-29<NA>노목느릅나무과(Ulmaceae)Zelkova serrata느티115015290시·군나무도로경상북도 김천시 감문면 남곡리 71536.219969128.1522222023-09-07
시도명시군구명관리기관명지정번호보호수지정일자보호수해지일자보호수유형명과명학명나무종류그루수나무나이나무높이가슴높이둘레품격명지목명소재지지번주소위도경도데이터기준일자
49경상북도김천시경상북도 김천시청11-26-361982-10-29<NA>노목느릅나무과(Ulmaceae)Zelkova serrata느티135017614시·군나무임야경상북도 김천시 증산면 동안리 402-135.911539128.0203942023-09-07
50경상북도김천시경상북도 김천시청2006-03-012006-05-22<NA>노목느릅나무과(Ulmaceae)Zelkova serrata느티150015650시·군나무임야경상북도 김천시 증산면 장전리 62235.867438128.0590632023-09-07
51경상북도김천시경상북도 김천시청11-3-20-2~61982-08-27<NA>노목느릅나무과(Ulmaceae)Zelkova serrata느티510019368시·군나무임야경상북도 김천시 부곡동 106436.121997128.0893952023-09-07
52경상북도김천시경상북도 김천시청11-3-20-11982-08-27<NA>노목느릅나무과(Ulmaceae)Zelkova serrata느티110011234시·군나무대경상북도 김천시 부곡동 106336.121976128.0897522023-09-07
53경상북도김천시경상북도 김천시청2011-03-011982-08-27<NA>노목장미과(Rosaceae)pyrus pyrifolia배130015150시·군나무대경상북도 김천시 부곡동 13936.115563128.1041872023-09-07
54경상북도김천시경상북도 김천시청2011-03-021994-10-21<NA>노목콩과(Leguminosae)Sophora japonicum회화130020620시·군나무대경상북도 김천시 백옥동 56836.11612128.0708052023-09-07
55경상북도김천시경상북도 김천시청11-3-15-11982-08-27<NA>노목느릅나무과(Ulmaceae)Zelkova serrata느티110016316시·군나무답경상북도 김천시 삼락동 112-1236.142168128.0960232023-09-07
56경상북도김천시경상북도 김천시청11-3-16-11982-08-27<NA>노목팽나무과(Celtidaceae)Celtis sinensis Persoon팽나무120013270시·군나무대경상북도 김천시 문당동 59236.155924128.0887932023-09-07
57경상북도김천시경상북도 김천시청11-3-16-21982-08-27<NA>노목느릅나무과(Ulmaceae)Zelkova serrata느티120013272시·군나무대경상북도 김천시 문당동 59236.155924128.0887932023-09-07
58경상북도김천시경상북도 김천시청2011-03-021994-10-21<NA>노목팽나무과(Celtidaceae)Celtis sinensis Persoon팽나무130020620시·군나무도로경상북도 김천시 덕곡동 1404-236.125345128.1503032023-09-07