Overview

Dataset statistics

Number of variables17
Number of observations69
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.8 KiB
Average record size in memory144.9 B

Variable types

Categorical9
Text2
Numeric6

Dataset

Description경기도 여주시의 보호수 현황입니다. 관리기관명,지정번호,보호수지정일자,보호수유형명,학명,나무종류,그루수,나무나이,나무높이,가슴높이둘레,나무갓지름,나무품격명,나무지목명,소재지지번주소,위도,경도 등의 데이터를 제공합니다.
Author경기도 여주시
URLhttps://www.data.go.kr/data/15105026/fileData.do

Alerts

관리기관명 has constant value ""Constant
보호수지정일자 has constant value ""Constant
보호수유형명 has constant value ""Constant
나무품격명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
그루수 is highly overall correlated with 학명 and 1 other fieldsHigh correlation
학명 is highly overall correlated with 나무종류 and 1 other fieldsHigh correlation
나무종류 is highly overall correlated with 학명 and 1 other fieldsHigh correlation
나무나이 is highly overall correlated with 가슴높이둘레 and 1 other fieldsHigh correlation
나무높이 is highly overall correlated with 가슴높이둘레 and 1 other fieldsHigh correlation
가슴높이둘레 is highly overall correlated with 나무나이 and 2 other fieldsHigh correlation
나무갓지름 is highly overall correlated with 나무나이 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
그루수 is highly imbalanced (89.1%)Imbalance
지정번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:50:52.099744
Analysis finished2023-12-12 11:50:57.303474
Duration5.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
경기도 여주시청
69 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도 여주시청
2nd row경기도 여주시청
3rd row경기도 여주시청
4th row경기도 여주시청
5th row경기도 여주시청

Common Values

ValueCountFrequency (%)
경기도 여주시청 69
100.0%

Length

2023-12-12T20:50:57.385728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:50:57.503445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 69
50.0%
여주시청 69
50.0%

지정번호
Text

UNIQUE 

Distinct69
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size684.0 B
2023-12-12T20:50:57.794602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.884058
Min length4

Characters and Unicode

Total characters337
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

Unique69 ?
Unique (%)100.0%

Sample

1st row여주-1
2nd row여주-2
3rd row여주-4
4th row여주-5
5th row여주-6
ValueCountFrequency (%)
여주-1 1
 
1.4%
여주-42 1
 
1.4%
여주-59 1
 
1.4%
여주-58 1
 
1.4%
여주-57 1
 
1.4%
여주-56 1
 
1.4%
여주-55 1
 
1.4%
여주-53 1
 
1.4%
여주-51 1
 
1.4%
여주-52 1
 
1.4%
Other values (59) 59
85.5%
2023-12-12T20:50:58.270364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
여 69
20.5%
주 69
20.5%
- 69
20.5%
1 18
 
5.3%
2 18
 
5.3%
5 16
 
4.7%
6 16
 
4.7%
7 16
 
4.7%
4 15
 
4.5%
3 12
 
3.6%
Other values (3) 19
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 138
40.9%
Decimal Number 130
38.6%
Dash Punctuation 69
20.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18
13.8%
2 18
13.8%
5 16
12.3%
6 16
12.3%
7 16
12.3%
4 15
11.5%
3 12
9.2%
9 8
6.2%
8 7
 
5.4%
0 4
 
3.1%
Other Letter
ValueCountFrequency (%)
여 69
50.0%
주 69
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 199
59.1%
Hangul 138
40.9%

Most frequent character per script

Common
ValueCountFrequency (%)
- 69
34.7%
1 18
 
9.0%
2 18
 
9.0%
5 16
 
8.0%
6 16
 
8.0%
7 16
 
8.0%
4 15
 
7.5%
3 12
 
6.0%
9 8
 
4.0%
8 7
 
3.5%
Hangul
ValueCountFrequency (%)
여 69
50.0%
주 69
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199
59.1%
Hangul 138
40.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
여 69
50.0%
주 69
50.0%
ASCII
ValueCountFrequency (%)
- 69
34.7%
1 18
 
9.0%
2 18
 
9.0%
5 16
 
8.0%
6 16
 
8.0%
7 16
 
8.0%
4 15
 
7.5%
3 12
 
6.0%
9 8
 
4.0%
8 7
 
3.5%

보호수지정일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
1982-10-15
69 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1982-10-15
2nd row1982-10-15
3rd row1982-10-15
4th row1982-10-15
5th row1982-10-15

Common Values

ValueCountFrequency (%)
1982-10-15 69
100.0%

Length

2023-12-12T20:50:58.429732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:50:58.559809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1982-10-15 69
100.0%

보호수유형명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
노목
69 

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 69
100.0%

Length

2023-12-12T20:50:58.689783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:50:58.841923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노목 69
100.0%

학명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size684.0 B
Zelkova serrata (Thunb.) Makino
33 
Juniperus chinensis L.
18 
Ginkgo biloba L.
15 
Alnus japonica (Thunb.) Steud.
 
1
Fraxinus rhynchophylla Hance
 
1

Length

Max length31
Median length30
Mean length24.942029
Min length4

Unique

Unique3 ?
Unique (%)4.3%

Sample

1st rowZelkova serrata (Thunb.) Makino
2nd rowGinkgo biloba L.
3rd rowJuniperus chinensis L.
4th rowJuniperus chinensis L.
5th rowJuniperus chinensis L.

Common Values

ValueCountFrequency (%)
Zelkova serrata (Thunb.) Makino 33
47.8%
Juniperus chinensis L. 18
26.1%
Ginkgo biloba L. 15
21.7%
Alnus japonica (Thunb.) Steud. 1
 
1.4%
Fraxinus rhynchophylla Hance 1
 
1.4%
<NA> 1
 
1.4%

Length

2023-12-12T20:50:58.996033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:50:59.179546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
thunb 34
14.2%
zelkova 33
13.8%
serrata 33
13.8%
makino 33
13.8%
l 33
13.8%
juniperus 18
7.5%
chinensis 18
7.5%
ginkgo 15
6.3%
biloba 15
6.3%
alnus 1
 
0.4%
Other values (6) 6
 
2.5%

나무종류
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size684.0 B
느티나무
33 
향나무
18 
은행나무
15 
오리나무
 
1
물푸레나무
 
1

Length

Max length5
Median length4
Mean length3.7681159
Min length3

Unique

Unique3 ?
Unique (%)4.3%

Sample

1st row느티나무
2nd row은행나무
3rd row향나무
4th row향나무
5th row향나무

Common Values

ValueCountFrequency (%)
느티나무 33
47.8%
향나무 18
26.1%
은행나무 15
21.7%
오리나무 1
 
1.4%
물푸레나무 1
 
1.4%
아까시나무 1
 
1.4%

Length

2023-12-12T20:50:59.375415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:50:59.555928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
느티나무 33
47.8%
향나무 18
26.1%
은행나무 15
21.7%
오리나무 1
 
1.4%
물푸레나무 1
 
1.4%
아까시나무 1
 
1.4%

그루수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size684.0 B
1
68 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
1 68
98.6%
3 1
 
1.4%

Length

2023-12-12T20:50:59.707695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:50:59.830431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 68
98.6%
3 1
 
1.4%

나무나이
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252.34783
Minimum100
Maximum700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-12T20:50:59.986375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1150
median200
Q3300
95-th percentile500
Maximum700
Range600
Interquartile range (IQR)150

Descriptive statistics

Standard deviation132.06859
Coefficient of variation (CV)0.52335933
Kurtosis1.2263363
Mean252.34783
Median Absolute Deviation (MAD)80
Skewness1.2119613
Sum17412
Variance17442.113
MonotonicityNot monotonic
2023-12-12T20:51:00.140440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
200 13
18.8%
150 11
15.9%
300 9
13.0%
250 6
8.7%
100 5
 
7.2%
500 5
 
7.2%
400 4
 
5.8%
120 4
 
5.8%
350 3
 
4.3%
160 2
 
2.9%
Other values (7) 7
10.1%
ValueCountFrequency (%)
100 5
 
7.2%
112 1
 
1.4%
120 4
 
5.8%
150 11
15.9%
160 2
 
2.9%
170 1
 
1.4%
180 1
 
1.4%
200 13
18.8%
250 6
8.7%
300 9
13.0%
ValueCountFrequency (%)
700 1
 
1.4%
600 1
 
1.4%
500 5
 
7.2%
430 1
 
1.4%
400 4
 
5.8%
350 3
 
4.3%
320 1
 
1.4%
300 9
13.0%
250 6
8.7%
200 13
18.8%

나무높이
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.246377
Minimum6
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-12T20:51:00.286782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7
Q112
median16
Q320
95-th percentile22.6
Maximum25
Range19
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.2563849
Coefficient of variation (CV)0.34476289
Kurtosis-0.86226001
Mean15.246377
Median Absolute Deviation (MAD)4
Skewness-0.21885584
Sum1052
Variance27.629582
MonotonicityNot monotonic
2023-12-12T20:51:00.450055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
18 10
14.5%
20 10
14.5%
15 7
10.1%
7 6
8.7%
13 5
 
7.2%
17 5
 
7.2%
22 4
 
5.8%
12 3
 
4.3%
16 3
 
4.3%
8 3
 
4.3%
Other values (7) 13
18.8%
ValueCountFrequency (%)
6 3
4.3%
7 6
8.7%
8 3
4.3%
9 2
 
2.9%
10 2
 
2.9%
11 1
 
1.4%
12 3
4.3%
13 5
7.2%
14 1
 
1.4%
15 7
10.1%
ValueCountFrequency (%)
25 3
 
4.3%
23 1
 
1.4%
22 4
 
5.8%
20 10
14.5%
18 10
14.5%
17 5
7.2%
16 3
 
4.3%
15 7
10.1%
14 1
 
1.4%
13 5
7.2%

가슴높이둘레
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean421.86957
Minimum111
Maximum982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-12T20:51:00.648799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile152.6
Q1271
median426
Q3548
95-th percentile707.8
Maximum982
Range871
Interquartile range (IQR)277

Descriptive statistics

Standard deviation193.91189
Coefficient of variation (CV)0.45964892
Kurtosis0.39628771
Mean421.86957
Median Absolute Deviation (MAD)138
Skewness0.59271317
Sum29109
Variance37601.821
MonotonicityNot monotonic
2023-12-12T20:51:00.836322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
426 2
 
2.9%
207 2
 
2.9%
358 2
 
2.9%
380 1
 
1.4%
213 1
 
1.4%
327 1
 
1.4%
376 1
 
1.4%
548 1
 
1.4%
324 1
 
1.4%
685 1
 
1.4%
Other values (56) 56
81.2%
ValueCountFrequency (%)
111 1
1.4%
124 1
1.4%
130 1
1.4%
141 1
1.4%
170 1
1.4%
179 1
1.4%
186 1
1.4%
192 1
1.4%
195 1
1.4%
200 1
1.4%
ValueCountFrequency (%)
982 1
1.4%
941 1
1.4%
895 1
1.4%
723 1
1.4%
685 1
1.4%
642 1
1.4%
619 1
1.4%
616 1
1.4%
611 1
1.4%
608 1
1.4%

나무갓지름
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6708696
Minimum0.33
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-12T20:51:01.019475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile0.508
Q10.9
median1.26
Q31.81
95-th percentile2.432
Maximum14
Range13.67
Interquartile range (IQR)0.91

Descriptive statistics

Standard deviation2.0706093
Coefficient of variation (CV)1.2392405
Kurtosis27.038908
Mean1.6708696
Median Absolute Deviation (MAD)0.42
Skewness5.0520442
Sum115.29
Variance4.2874228
MonotonicityNot monotonic
2023-12-12T20:51:01.210974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.16 3
 
4.3%
1.6 3
 
4.3%
0.62 3
 
4.3%
1.52 3
 
4.3%
2.08 2
 
2.9%
1.83 2
 
2.9%
1.7 2
 
2.9%
1.02 2
 
2.9%
2.3 2
 
2.9%
0.68 2
 
2.9%
Other values (40) 45
65.2%
ValueCountFrequency (%)
0.33 1
 
1.4%
0.4 1
 
1.4%
0.44 1
 
1.4%
0.5 1
 
1.4%
0.52 1
 
1.4%
0.58 1
 
1.4%
0.59 1
 
1.4%
0.62 3
4.3%
0.65 1
 
1.4%
0.68 2
2.9%
ValueCountFrequency (%)
14.0 1
1.4%
12.0 1
1.4%
3.7 1
1.4%
2.52 1
1.4%
2.3 2
2.9%
2.26 1
1.4%
2.24 1
1.4%
2.08 2
2.9%
2.0 1
1.4%
1.92 1
1.4%

나무품격명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
마을나무
69 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
마을나무 69
100.0%

Length

2023-12-12T20:51:01.381403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:51:01.479014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마을나무 69
100.0%

나무지목명
Categorical

Distinct11
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size684.0 B
대
20 
임야
13 
전
12 
도로
6 
답
5 
Other values (6)
13 

Length

Max length4
Median length1
Mean length1.5652174
Min length1

Unique

Unique3 ?
Unique (%)4.3%

Sample

1st row하천
2nd row대
3rd row대
4th row대
5th row도로

Common Values

ValueCountFrequency (%)
대 20
29.0%
임야 13
18.8%
전 12
17.4%
도로 6
 
8.7%
답 5
 
7.2%
구거 5
 
7.2%
하천 3
 
4.3%
종교용지 2
 
2.9%
유지 1
 
1.4%
잡종지 1
 
1.4%

Length

2023-12-12T20:51:01.586960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대 20
29.0%
임야 13
18.8%
전 12
17.4%
도로 6
 
8.7%
답 5
 
7.2%
구거 5
 
7.2%
하천 3
 
4.3%
종교용지 2
 
2.9%
유지 1
 
1.4%
잡종지 1
 
1.4%

소재지지번주소
Text

Distinct59
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Memory size684.0 B
2023-12-12T20:51:01.881149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length19.666667
Min length6

Characters and Unicode

Total characters1357
Distinct characters89
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

Unique54 ?
Unique (%)78.3%

Sample

1st row경기도 여주시 우만동 233
2nd row경기도 여주시 교동 261-1
3rd row경기도 여주시 교동 261-1
4th row경기도 여주시 교동 261-1
5th row경기도 여주시 멱곡동 492
ValueCountFrequency (%)
경기도 68
20.1%
여주시 68
20.1%
대신면 14
 
4.1%
산 10
 
2.9%
흥천면 10
 
2.9%
금사면 8
 
2.4%
초현리 6
 
1.8%
12-15 6
 
1.8%
북내면 5
 
1.5%
산북면 5
 
1.5%
Other values (106) 139
41.0%
2023-12-12T20:51:02.318987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
270
19.9%
도 71
 
5.2%
주 71
 
5.2%
경 68
 
5.0%
여 68
 
5.0%
시 68
 
5.0%
기 68
 
5.0%
리 57
 
4.2%
면 54
 
4.0%
1 52
 
3.8%
Other values (79) 510
37.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 803
59.2%
Space Separator 270
 
19.9%
Decimal Number 240
 
17.7%
Dash Punctuation 44
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 71
 
8.8%
주 71
 
8.8%
경 68
 
8.5%
여 68
 
8.5%
시 68
 
8.5%
기 68
 
8.5%
리 57
 
7.1%
면 54
 
6.7%
대 22
 
2.7%
산 17
 
2.1%
Other values (67) 239
29.8%
Decimal Number
ValueCountFrequency (%)
1 52
21.7%
2 51
21.2%
4 26
10.8%
3 23
9.6%
5 22
9.2%
7 17
 
7.1%
6 14
 
5.8%
8 14
 
5.8%
9 12
 
5.0%
0 9
 
3.8%
Space Separator
ValueCountFrequency (%)
270
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 803
59.2%
Common 554
40.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 71
 
8.8%
주 71
 
8.8%
경 68
 
8.5%
여 68
 
8.5%
시 68
 
8.5%
기 68
 
8.5%
리 57
 
7.1%
면 54
 
6.7%
대 22
 
2.7%
산 17
 
2.1%
Other values (67) 239
29.8%
Common
ValueCountFrequency (%)
270
48.7%
1 52
 
9.4%
2 51
 
9.2%
- 44
 
7.9%
4 26
 
4.7%
3 23
 
4.2%
5 22
 
4.0%
7 17
 
3.1%
6 14
 
2.5%
8 14
 
2.5%
Other values (2) 21
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 803
59.2%
ASCII 554
40.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
270
48.7%
1 52
 
9.4%
2 51
 
9.2%
- 44
 
7.9%
4 26
 
4.7%
3 23
 
4.2%
5 22
 
4.0%
7 17
 
3.1%
6 14
 
2.5%
8 14
 
2.5%
Other values (2) 21
 
3.8%
Hangul
ValueCountFrequency (%)
도 71
 
8.8%
주 71
 
8.8%
경 68
 
8.5%
여 68
 
8.5%
시 68
 
8.5%
기 68
 
8.5%
리 57
 
7.1%
면 54
 
6.7%
대 22
 
2.7%
산 17
 
2.1%
Other values (67) 239
29.8%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.328736
Minimum37.15587
Maximum37.42344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-12T20:51:02.475931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.15587
5-th percentile37.197594
Q137.29277
median37.34505
Q337.379271
95-th percentile37.41463
Maximum37.42344
Range0.26757
Interquartile range (IQR)0.08650071

Descriptive statistics

Standard deviation0.066376019
Coefficient of variation (CV)0.001778148
Kurtosis-0.076576102
Mean37.328736
Median Absolute Deviation (MAD)0.04376
Skewness-0.78177312
Sum2575.6828
Variance0.0044057758
MonotonicityNot monotonic
2023-12-12T20:51:02.639461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.37927071 6
 
8.7%
37.37845 2
 
2.9%
37.37555 1
 
1.4%
37.37874 1
 
1.4%
37.38339 1
 
1.4%
37.37628 1
 
1.4%
37.41659 1
 
1.4%
37.42344 1
 
1.4%
37.40777 1
 
1.4%
37.41577 1
 
1.4%
Other values (53) 53
76.8%
ValueCountFrequency (%)
37.15587 1
1.4%
37.17265 1
1.4%
37.18296 1
1.4%
37.19513 1
1.4%
37.20129 1
1.4%
37.20233 1
1.4%
37.22556 1
1.4%
37.22849 1
1.4%
37.25535 1
1.4%
37.25537 1
1.4%
ValueCountFrequency (%)
37.42344 1
1.4%
37.41659 1
1.4%
37.41585 1
1.4%
37.41577 1
1.4%
37.41292 1
1.4%
37.41104 1
1.4%
37.40777 1
1.4%
37.401639 1
1.4%
37.39593 1
1.4%
37.39302 1
1.4%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.5926
Minimum127.42425
Maximum127.71834
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-12T20:51:02.789795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.42425
5-th percentile127.45684
Q1127.54004
median127.59641
Q3127.65364
95-th percentile127.71004
Maximum127.71834
Range0.29409
Interquartile range (IQR)0.1136

Descriptive statistics

Standard deviation0.074972237
Coefficient of variation (CV)0.00058759081
Kurtosis-0.54535021
Mean127.5926
Median Absolute Deviation (MAD)0.0572331
Skewness-0.30095036
Sum8803.8891
Variance0.0056208363
MonotonicityNot monotonic
2023-12-12T20:51:02.942688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.5964069 6
 
8.7%
127.5064 1
 
1.4%
127.51957 1
 
1.4%
127.47281 1
 
1.4%
127.45628 1
 
1.4%
127.45769 1
 
1.4%
127.44297 1
 
1.4%
127.42425 1
 
1.4%
127.42446 1
 
1.4%
127.63863 1
 
1.4%
Other values (54) 54
78.3%
ValueCountFrequency (%)
127.42425 1
1.4%
127.42446 1
1.4%
127.44297 1
1.4%
127.45628 1
1.4%
127.45769 1
1.4%
127.47281 1
1.4%
127.49662 1
1.4%
127.50603 1
1.4%
127.50637 1
1.4%
127.5064 1
1.4%
ValueCountFrequency (%)
127.71834 1
1.4%
127.71743 1
1.4%
127.7143 1
1.4%
127.71244 1
1.4%
127.70644 1
1.4%
127.69374 1
1.4%
127.69303 1
1.4%
127.6805 1
1.4%
127.67582 1
1.4%
127.67577 1
1.4%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
2023-11-15
69 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-11-15
2nd row2023-11-15
3rd row2023-11-15
4th row2023-11-15
5th row2023-11-15

Common Values

ValueCountFrequency (%)
2023-11-15 69
100.0%

Length

2023-12-12T20:51:03.106171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:51:03.276739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-11-15 69
100.0%

Interactions

2023-12-12T20:50:56.042640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:52.731791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:53.362332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:54.048964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:54.697058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:55.369000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:56.154536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:52.827047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:53.469248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:54.177090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:54.803225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:55.482090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:56.276192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:52.939575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:53.574948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:54.291013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:54.927410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:55.601517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:56.380702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:53.040526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:53.677524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:54.387806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:55.028791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:55.698654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:56.766124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:53.136209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:53.800380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:54.484574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:55.139026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:55.821933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:56.847574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:53.237809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:53.915419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:54.582769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:55.248633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:50:55.922521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:51:03.407586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정번호학명나무종류그루수나무나이나무높이가슴높이둘레나무갓지름나무지목명소재지지번주소위도경도
지정번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
학명1.0001.0001.0001.0000.0000.8080.5590.1550.4030.9700.0000.424
나무종류1.0001.0001.0001.0000.0000.6430.5580.6410.7080.9820.0000.317
그루수1.0001.0001.0001.0000.0000.0000.0000.0000.2351.0000.0000.000
나무나이1.0000.0000.0000.0001.0000.4120.3760.4330.6940.8570.5930.494
나무높이1.0000.8080.6430.0000.4121.0000.5920.5630.1500.6360.4260.435
가슴높이둘레1.0000.5590.5580.0000.3760.5921.0000.6300.2650.8760.0000.277
나무갓지름1.0000.1550.6410.0000.4330.5630.6301.0000.6880.9970.3890.575
나무지목명1.0000.4030.7080.2350.6940.1500.2650.6881.0001.0000.6490.334
소재지지번주소1.0000.9700.9821.0000.8570.6360.8760.9971.0001.0001.0001.000
위도1.0000.0000.0000.0000.5930.4260.0000.3890.6491.0001.0000.787
경도1.0000.4240.3170.0000.4940.4350.2770.5750.3341.0000.7871.000
2023-12-12T20:51:03.568281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
나무지목명그루수학명나무종류
나무지목명1.0000.2040.1660.442
그루수0.2041.0000.9770.970
학명0.1660.9771.0001.000
나무종류0.4420.9701.0001.000
2023-12-12T20:51:03.711081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
나무나이나무높이가슴높이둘레나무갓지름위도경도학명나무종류그루수나무지목명
나무나이1.0000.3900.5320.5280.070-0.0660.0000.0000.0000.396
나무높이0.3901.0000.7670.697-0.019-0.0620.4450.3920.0000.040
가슴높이둘레0.5320.7671.0000.8430.049-0.0390.3510.3060.0000.111
나무갓지름0.5280.6970.8431.0000.124-0.1080.1220.4960.0000.443
위도0.070-0.0190.0490.1241.000-0.6260.0000.0000.0000.338
경도-0.066-0.062-0.039-0.108-0.6261.0000.1760.1610.0000.139
학명0.0000.4450.3510.1220.0000.1761.0001.0000.9770.166
나무종류0.0000.3920.3060.4960.0000.1611.0001.0000.9700.442
그루수0.0000.0000.0000.0000.0000.0000.9770.9701.0000.204
나무지목명0.3960.0400.1110.4430.3380.1390.1660.4420.2041.000

Missing values

2023-12-12T20:50:57.007442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:50:57.226746image/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

관리기관명지정번호보호수지정일자보호수유형명학명나무종류그루수나무나이나무높이가슴높이둘레나무갓지름나무품격명나무지목명소재지지번주소위도경도데이터기준일자
0경기도 여주시청여주-11982-10-15노목Zelkova serrata (Thunb.) Makino느티나무1300186852.08마을나무하천경기도 여주시 우만동 23337.25587127.68052023-11-15
1경기도 여주시청여주-21982-10-15노목Ginkgo biloba L.은행나무1200203201.16마을나무대경기도 여주시 교동 261-137.28925127.628412023-11-15
2경기도 여주시청여주-41982-10-15노목Juniperus chinensis L.향나무110061790.44마을나무대경기도 여주시 교동 261-137.28924127.628162023-11-15
3경기도 여주시청여주-51982-10-15노목Juniperus chinensis L.향나무115061240.33마을나무대경기도 여주시 교동 261-137.28929127.628072023-11-15
4경기도 여주시청여주-61982-10-15노목Juniperus chinensis L.향나무125073581.0마을나무도로경기도 여주시 멱곡동 49237.25537127.666182023-11-15
5경기도 여주시청여주-71982-10-15노목Zelkova serrata (Thunb.) Makino느티나무1170153081.16마을나무도로경기도 여주시 멱곡동 49237.25535127.666652023-11-15
6경기도 여주시청여주-81982-10-15노목Ginkgo biloba L.은행나무1100132990.9마을나무대경기도 여주시 홍문동 437.29799127.637582023-11-15
7경기도 여주시청여주-91982-10-15노목Ginkgo biloba L.은행나무1700255812.26마을나무전경기도 여주시 점동면 뇌곡리 42-237.15587127.666472023-11-15
8경기도 여주시청여주-101982-10-15노목Zelkova serrata (Thunb.) Makino느티나무1430133491.18마을나무유지경기도 여주시 점동면 덕평리 8337.18296127.660722023-11-15
9경기도 여주시청여주-111982-10-15노목Ginkgo biloba L.은행나무1150228950.9마을나무잡종지경기도 여주시 점동면 덕평리 387-3337.17265127.649922023-11-15
관리기관명지정번호보호수지정일자보호수유형명학명나무종류그루수나무나이나무높이가슴높이둘레나무갓지름나무품격명나무지목명소재지지번주소위도경도데이터기준일자
59경기도 여주시청여주-691982-10-15노목Ginkgo biloba L.은행나무1300235261.66마을나무전경기도 여주시 북내면 중암리 44137.34746127.706442023-11-15
60경기도 여주시청여주-711982-10-15노목Zelkova serrata (Thunb.) Makino느티나무1200174261.0마을나무구거경기도 여주시 북내면 내룡리 231-437.3788127.675822023-11-15
61경기도 여주시청여주-721982-10-15노목Zelkova serrata (Thunb.) Makino느티나무1200133521.26마을나무구거경기도 여주시 북내면 내룡리 246-537.37845127.675772023-11-15
62경기도 여주시청여주-731982-10-15노목Zelkova serrata (Thunb.) Makino느티나무1250205391.6마을나무답경기도 여주시 북내면 가정리 127-137.29925127.693032023-11-15
63경기도 여주시청여주-741982-10-15노목Juniperus chinensis L.향나무115082071.5마을나무전경기도 여주시 현암동 320-237.31476127.636752023-11-15
64경기도 여주시청여주-751982-10-15노목Ginkgo biloba L.은행나무1500229823.7마을나무대경기도 여주시 강천면 간매리 485-237.26714127.71432023-11-15
65경기도 여주시청여주-761982-10-15노목Zelkova serrata (Thunb.) Makino느티나무1200184601.34마을나무도로경기도 여주시 강천면 간매리 420-737.27207127.717432023-11-15
66경기도 여주시청여주-771982-10-15노목Zelkova serrata (Thunb.) Makino느티나무1200176081.86마을나무대경기도 여주시 강천면 간매리 425-137.27102127.718342023-11-15
67경기도 여주시청여주-781982-10-15노목<NA>아까시나무11121645914.0마을나무학교용지천남초등학교37.34569127.631532023-11-15
68경기도 여주시청여주-791982-10-15노목Zelkova serrata (Thunb.) Makino느티나무12501852412.0마을나무답경기도 여주시 대신면 상구리 45837.37291127.641432023-11-15