Overview

Dataset statistics

Number of variables15
Number of observations96
Missing cells95
Missing cells (%)6.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.1 KiB
Average record size in memory129.4 B

Variable types

Categorical6
Text3
Numeric6

Dataset

Description광주광역시에서 산림재해를 예방하기 위해 지정하여 관리하고 있는 2022년 9월 기준 자치구별 산사태취약지역 지정 현황입니다.
Author광주광역시
URLhttps://www.data.go.kr/data/15012196/fileData.do

Alerts

조사지(시도) has constant value ""Constant
기타지번 has constant value ""Constant
위도(도) has constant value ""Constant
관할 is highly overall correlated with 경도(분) and 1 other fieldsHigh correlation
조사지(시군구) is highly overall correlated with 경도(분) and 1 other fieldsHigh correlation
경도(분) is highly overall correlated with 관할 and 2 other fieldsHigh correlation
경도(도) is highly overall correlated with 경도(분)High correlation
경도(도) is highly imbalanced (91.6%)Imbalance
기타지번 has 95 (99.0%) missing valuesMissing
경도(분) has 1 (1.0%) zerosZeros
지정면적(제곱미터) has 10 (10.4%) zerosZeros
거리(미터) has 50 (52.1%) zerosZeros

Reproduction

Analysis started2023-12-12 21:01:40.777174
Analysis finished2023-12-12 21:01:44.813003
Duration4.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관할
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size900.0 B
광주광역시 광산구
38 
광주광역시 동구
21 
광주광역시 북구
20 
광주광역시 남구
13 
광주광역시 서구
4 

Length

Max length9
Median length8
Mean length8.3958333
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광주광역시 북구
2nd row광주광역시 동구
3rd row광주광역시 동구
4th row광주광역시 광산구
5th row광주광역시 북구

Common Values

ValueCountFrequency (%)
광주광역시 광산구 38
39.6%
광주광역시 동구 21
21.9%
광주광역시 북구 20
20.8%
광주광역시 남구 13
 
13.5%
광주광역시 서구 4
 
4.2%

Length

2023-12-13T06:01:44.876702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:01:44.970459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주광역시 96
50.0%
광산구 38
 
19.8%
동구 21
 
10.9%
북구 20
 
10.4%
남구 13
 
6.8%
서구 4
 
2.1%

조사지(시도)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
광주광역시
96 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광주광역시 96
100.0%

Length

2023-12-13T06:01:45.069307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:01:45.145749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주광역시 96
100.0%

조사지(시군구)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size900.0 B
광산구
38 
동구
21 
북구
20 
남구
13 
서구
4 

Length

Max length3
Median length2
Mean length2.3958333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row북구
2nd row동구
3rd row동구
4th row광산구
5th row북구

Common Values

ValueCountFrequency (%)
광산구 38
39.6%
동구 21
21.9%
북구 20
20.8%
남구 13
 
13.5%
서구 4
 
4.2%

Length

2023-12-13T06:01:45.233556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:01:45.340307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광산구 38
39.6%
동구 21
21.9%
북구 20
20.8%
남구 13
 
13.5%
서구 4
 
4.2%

조사지(동)
Text

Distinct48
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023-12-13T06:01:45.525923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9583333
Min length2

Characters and Unicode

Total characters284
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)17.7%

Sample

1st row신안동
2nd row월남동
3rd row용산동
4th row박호동
5th row각화동
ValueCountFrequency (%)
광산동 6
 
6.2%
운림동 5
 
5.2%
월남동 4
 
4.2%
두암동 3
 
3.1%
지산동 3
 
3.1%
임곡동 3
 
3.1%
주월동 3
 
3.1%
박호동 3
 
3.1%
북산동 3
 
3.1%
명도동 3
 
3.1%
Other values (38) 60
62.5%
2023-12-13T06:01:45.826488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 97
34.2%
산 23
 
8.1%
월 15
 
5.3%
림 7
 
2.5%
남 7
 
2.5%
암 7
 
2.5%
두 7
 
2.5%
광 6
 
2.1%
호 6
 
2.1%
임 5
 
1.8%
Other values (47) 104
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 284
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 97
34.2%
산 23
 
8.1%
월 15
 
5.3%
림 7
 
2.5%
남 7
 
2.5%
암 7
 
2.5%
두 7
 
2.5%
광 6
 
2.1%
호 6
 
2.1%
임 5
 
1.8%
Other values (47) 104
36.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 284
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 97
34.2%
산 23
 
8.1%
월 15
 
5.3%
림 7
 
2.5%
남 7
 
2.5%
암 7
 
2.5%
두 7
 
2.5%
광 6
 
2.1%
호 6
 
2.1%
임 5
 
1.8%
Other values (47) 104
36.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 284
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 97
34.2%
산 23
 
8.1%
월 15
 
5.3%
림 7
 
2.5%
남 7
 
2.5%
암 7
 
2.5%
두 7
 
2.5%
광 6
 
2.1%
호 6
 
2.1%
임 5
 
1.8%
Other values (47) 104
36.6%

지번
Text

Distinct94
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023-12-13T06:01:46.105019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length5.34375
Min length3

Characters and Unicode

Total characters513
Distinct characters24
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

Unique92 ?
Unique (%)95.8%

Sample

1st row310-6대
2nd row609-2대
3rd row산1-2
4th row산156-1임
5th row산20-3임
ValueCountFrequency (%)
산 8
 
7.1%
임 7
 
6.2%
산29임 2
 
1.8%
135 2
 
1.8%
산56-1임 1
 
0.9%
산67임 1
 
0.9%
산97임 1
 
0.9%
산95임 1
 
0.9%
200전 1
 
0.9%
143-1 1
 
0.9%
Other values (87) 87
77.7%
2023-12-13T06:01:46.496634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 74
14.4%
임 66
12.9%
1 65
12.7%
- 47
9.2%
2 40
 
7.8%
3 25
 
4.9%
8 25
 
4.9%
9 24
 
4.7%
4 23
 
4.5%
5 23
 
4.5%
Other values (14) 101
19.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 285
55.6%
Other Letter 165
32.2%
Dash Punctuation 47
 
9.2%
Space Separator 16
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 74
44.8%
임 66
40.0%
전 5
 
3.0%
대 4
 
2.4%
답 3
 
1.8%
구 3
 
1.8%
번 2
 
1.2%
지 2
 
1.2%
도 2
 
1.2%
종 2
 
1.2%
Other values (2) 2
 
1.2%
Decimal Number
ValueCountFrequency (%)
1 65
22.8%
2 40
14.0%
3 25
 
8.8%
8 25
 
8.8%
9 24
 
8.4%
4 23
 
8.1%
5 23
 
8.1%
6 22
 
7.7%
0 21
 
7.4%
7 17
 
6.0%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 348
67.8%
Hangul 165
32.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 74
44.8%
임 66
40.0%
전 5
 
3.0%
대 4
 
2.4%
답 3
 
1.8%
구 3
 
1.8%
번 2
 
1.2%
지 2
 
1.2%
도 2
 
1.2%
종 2
 
1.2%
Other values (2) 2
 
1.2%
Common
ValueCountFrequency (%)
1 65
18.7%
- 47
13.5%
2 40
11.5%
3 25
 
7.2%
8 25
 
7.2%
9 24
 
6.9%
4 23
 
6.6%
5 23
 
6.6%
6 22
 
6.3%
0 21
 
6.0%
Other values (2) 33
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 348
67.8%
Hangul 165
32.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 74
44.8%
임 66
40.0%
전 5
 
3.0%
대 4
 
2.4%
답 3
 
1.8%
구 3
 
1.8%
번 2
 
1.2%
지 2
 
1.2%
도 2
 
1.2%
종 2
 
1.2%
Other values (2) 2
 
1.2%
ASCII
ValueCountFrequency (%)
1 65
18.7%
- 47
13.5%
2 40
11.5%
3 25
 
7.2%
8 25
 
7.2%
9 24
 
6.9%
4 23
 
6.6%
5 23
 
6.6%
6 22
 
6.3%
0 21
 
6.0%
Other values (2) 33
9.5%

기타지번
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing95
Missing (%)99.0%
Memory size900.0 B
2023-12-13T06:01:46.624868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters5
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

Unique1 ?
Unique (%)100.0%

Sample

1st row산121 임
ValueCountFrequency (%)
산121 1
50.0%
임 1
50.0%
2023-12-13T06:01:46.891637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
산 1
16.7%
2 1
16.7%
1
16.7%
임 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3
50.0%
Other Letter 2
33.3%
Space Separator 1
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
66.7%
2 1
33.3%
Other Letter
ValueCountFrequency (%)
산 1
50.0%
임 1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4
66.7%
Hangul 2
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2
50.0%
2 1
25.0%
1
25.0%
Hangul
ValueCountFrequency (%)
산 1
50.0%
임 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
66.7%
Hangul 2
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2
50.0%
2 1
25.0%
1
25.0%
Hangul
ValueCountFrequency (%)
산 1
50.0%
임 1
50.0%

위도(도)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
35
96 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row35
2nd row35
3rd row35
4th row35
5th row35

Common Values

ValueCountFrequency (%)
35 96
100.0%

Length

2023-12-13T06:01:47.034164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:01:47.125785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
35 96
100.0%

위도(분)
Real number (ℝ)

Distinct12
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1354167
Minimum4
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-13T06:01:47.211492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q17
median9
Q311
95-th percentile14
Maximum15
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7323396
Coefficient of variation (CV)0.29909305
Kurtosis-0.95284582
Mean9.1354167
Median Absolute Deviation (MAD)2
Skewness0.11100491
Sum877
Variance7.4656798
MonotonicityNot monotonic
2023-12-13T06:01:47.311032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 13
13.5%
9 13
13.5%
10 12
12.5%
12 12
12.5%
7 10
10.4%
11 10
10.4%
5 8
8.3%
8 7
7.3%
14 6
6.2%
13 3
 
3.1%
Other values (2) 2
 
2.1%
ValueCountFrequency (%)
4 1
 
1.0%
5 8
8.3%
6 13
13.5%
7 10
10.4%
8 7
7.3%
9 13
13.5%
10 12
12.5%
11 10
10.4%
12 12
12.5%
13 3
 
3.1%
ValueCountFrequency (%)
15 1
 
1.0%
14 6
6.2%
13 3
 
3.1%
12 12
12.5%
11 10
10.4%
10 12
12.5%
9 13
13.5%
8 7
7.3%
7 10
10.4%
6 13
13.5%

위도(초)
Real number (ℝ)

Distinct95
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.84125
Minimum0.482
Maximum59.546
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-13T06:01:47.443126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.482
5-th percentile5.649
Q117.72425
median32.1885
Q346.474
95-th percentile58.299
Maximum59.546
Range59.064
Interquartile range (IQR)28.74975

Descriptive statistics

Standard deviation17.598918
Coefficient of variation (CV)0.55270814
Kurtosis-1.2703697
Mean31.84125
Median Absolute Deviation (MAD)14.539
Skewness-0.0073536794
Sum3056.76
Variance309.72192
MonotonicityNot monotonic
2023-12-13T06:01:47.563839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.199 2
 
2.1%
22.5 1
 
1.0%
0.999 1
 
1.0%
58.199 1
 
1.0%
25.922 1
 
1.0%
20.804 1
 
1.0%
22.599 1
 
1.0%
40.359 1
 
1.0%
43.599 1
 
1.0%
45.269 1
 
1.0%
Other values (85) 85
88.5%
ValueCountFrequency (%)
0.482 1
1.0%
0.999 1
1.0%
3.12 1
1.0%
3.699 1
1.0%
5.499 1
1.0%
5.699 1
1.0%
5.7 1
1.0%
6.198 1
1.0%
6.899 1
1.0%
8.263 1
1.0%
ValueCountFrequency (%)
59.546 1
1.0%
59.499 1
1.0%
59.2 1
1.0%
59.199 1
1.0%
58.599 1
1.0%
58.199 1
1.0%
57.61 1
1.0%
57.407 1
1.0%
55.956 1
1.0%
55.799 1
1.0%

경도(도)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size900.0 B
126
95 
127
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row126
2nd row126
3rd row126
4th row126
5th row126

Common Values

ValueCountFrequency (%)
126 95
99.0%
127 1
 
1.0%

Length

2023-12-13T06:01:47.965919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:01:48.048226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
126 95
99.0%
127 1
 
1.0%

경도(분)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.75
Minimum0
Maximum58
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-13T06:01:48.130077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41
Q144
median52
Q356
95-th percentile57
Maximum58
Range58
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.9312838
Coefficient of variation (CV)0.15942279
Kurtosis14.780186
Mean49.75
Median Absolute Deviation (MAD)5
Skewness-2.6821967
Sum4776
Variance62.905263
MonotonicityNot monotonic
2023-12-13T06:01:48.246675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
44 15
15.6%
57 15
15.6%
56 13
13.5%
55 8
8.3%
52 6
 
6.2%
53 5
 
5.2%
45 5
 
5.2%
41 5
 
5.2%
42 4
 
4.2%
50 4
 
4.2%
Other values (8) 16
16.7%
ValueCountFrequency (%)
0 1
 
1.0%
39 2
 
2.1%
40 1
 
1.0%
41 5
 
5.2%
42 4
 
4.2%
43 3
 
3.1%
44 15
15.6%
45 5
 
5.2%
46 1
 
1.0%
49 3
 
3.1%
ValueCountFrequency (%)
58 2
 
2.1%
57 15
15.6%
56 13
13.5%
55 8
8.3%
54 3
 
3.1%
53 5
 
5.2%
52 6
 
6.2%
50 4
 
4.2%
49 3
 
3.1%
46 1
 
1.0%

경도(초)
Real number (ℝ)

Distinct90
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.956677
Minimum0.938
Maximum59.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-13T06:01:48.383718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.938
5-th percentile5.425
Q116.565
median30.85
Q342.10775
95-th percentile55.65
Maximum59.5
Range58.562
Interquartile range (IQR)25.54275

Descriptive statistics

Standard deviation15.717629
Coefficient of variation (CV)0.52467867
Kurtosis-0.87820956
Mean29.956677
Median Absolute Deviation (MAD)12.3895
Skewness-0.12937632
Sum2875.841
Variance247.04388
MonotonicityNot monotonic
2023-12-13T06:01:48.500569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.5 3
 
3.1%
28.1 2
 
2.1%
27.8 2
 
2.1%
7.6 2
 
2.1%
28.6 2
 
2.1%
29.3 1
 
1.0%
14.606 1
 
1.0%
30.225 1
 
1.0%
44.949 1
 
1.0%
13.8 1
 
1.0%
Other values (80) 80
83.3%
ValueCountFrequency (%)
0.938 1
1.0%
1.0 1
1.0%
1.399 1
1.0%
1.455 1
1.0%
4.6 1
1.0%
5.7 1
1.0%
6.0 1
1.0%
7.44 1
1.0%
7.6 2
2.1%
7.8 1
1.0%
ValueCountFrequency (%)
59.5 1
1.0%
58.9 1
1.0%
57.6 1
1.0%
56.583 1
1.0%
55.8 1
1.0%
55.6 1
1.0%
55.016 1
1.0%
53.8 1
1.0%
51.297 1
1.0%
49.8 1
1.0%

취약지역유형
Categorical

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size900.0 B
토석류
68 
산사태
28 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row토석류
2nd row토석류
3rd row산사태
4th row토석류
5th row토석류

Common Values

ValueCountFrequency (%)
토석류 68
70.8%
산사태 28
29.2%

Length

2023-12-13T06:01:48.630910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:01:48.707387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
토석류 68
70.8%
산사태 28
29.2%

지정면적(제곱미터)
Real number (ℝ)

ZEROS 

Distinct86
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2653.1374
Minimum0
Maximum65346
Zeros10
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-13T06:01:48.804064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1352.25
median1188.03
Q32822.8
95-th percentile6441.5
Maximum65346
Range65346
Interquartile range (IQR)2470.55

Descriptive statistics

Standard deviation7039.0759
Coefficient of variation (CV)2.653114
Kurtosis68.029573
Mean2653.1374
Median Absolute Deviation (MAD)990.7
Skewness7.7937884
Sum254701.19
Variance49548590
MonotonicityNot monotonic
2023-12-13T06:01:48.933096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.4%
50.68 2
 
2.1%
350.0 1
 
1.0%
177.0 1
 
1.0%
2635.23 1
 
1.0%
629.2 1
 
1.0%
1420.8 1
 
1.0%
88.07 1
 
1.0%
722.4 1
 
1.0%
523.6 1
 
1.0%
Other values (76) 76
79.2%
ValueCountFrequency (%)
0.0 10
10.4%
0.09 1
 
1.0%
50.68 2
 
2.1%
60.21 1
 
1.0%
78.0 1
 
1.0%
88.07 1
 
1.0%
164.0 1
 
1.0%
177.0 1
 
1.0%
248.0 1
 
1.0%
248.9 1
 
1.0%
ValueCountFrequency (%)
65346.0 1
1.0%
21333.0 1
1.0%
8064.42 1
1.0%
6915.0 1
1.0%
6662.0 1
1.0%
6368.0 1
1.0%
6333.2 1
1.0%
6330.0 1
1.0%
5708.0 1
1.0%
5356.0 1
1.0%

거리(미터)
Real number (ℝ)

ZEROS 

Distinct29
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.02083
Minimum0
Maximum1400
Zeros50
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-13T06:01:49.055491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3100
95-th percentile900
Maximum1400
Range1400
Interquartile range (IQR)100

Descriptive statistics

Standard deviation291.71893
Coefficient of variation (CV)2.1446636
Kurtosis6.5413975
Mean136.02083
Median Absolute Deviation (MAD)0
Skewness2.6734783
Sum13058
Variance85099.936
MonotonicityNot monotonic
2023-12-13T06:01:49.157611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 50
52.1%
100 6
 
6.2%
50 5
 
5.2%
30 3
 
3.1%
900 3
 
3.1%
60 3
 
3.1%
300 2
 
2.1%
1000 2
 
2.1%
700 2
 
2.1%
220 1
 
1.0%
Other values (19) 19
 
19.8%
ValueCountFrequency (%)
0 50
52.1%
10 1
 
1.0%
20 1
 
1.0%
22 1
 
1.0%
25 1
 
1.0%
28 1
 
1.0%
30 3
 
3.1%
33 1
 
1.0%
50 5
 
5.2%
60 3
 
3.1%
ValueCountFrequency (%)
1400 1
 
1.0%
1200 1
 
1.0%
1000 2
2.1%
900 3
3.1%
700 2
2.1%
500 1
 
1.0%
450 1
 
1.0%
400 1
 
1.0%
300 2
2.1%
220 1
 
1.0%

Interactions

2023-12-13T06:01:44.014956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:41.415205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:41.969437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:42.511563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:43.059726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:43.530487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:44.092165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:41.493920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:42.053975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:42.588305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:43.136640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:43.609338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:44.182238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:41.581529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:42.134661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:42.686539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:43.216470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:43.686459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:44.264387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:41.656876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:42.213165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:42.778401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:43.288944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:43.767186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:44.355618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:41.738612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:42.312524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:42.878319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:43.360216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:43.841652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:44.435028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:41.880321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:42.416015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:42.975314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:43.444830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:43.923860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:01:49.243979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관할조사지(시군구)조사지(동)지번위도(분)위도(초)경도(도)경도(분)경도(초)취약지역유형지정면적(제곱미터)거리(미터)
관할1.0001.0000.9980.9730.7880.0000.0000.8940.4440.2220.0000.693
조사지(시군구)1.0001.0000.9980.9730.7880.0000.0000.8940.4440.2220.0000.693
조사지(동)0.9980.9981.0000.9990.9770.6881.0000.9980.7870.6840.4750.826
지번0.9730.9730.9991.0000.9700.8171.0000.9860.9420.5471.0001.000
위도(분)0.7880.7880.9770.9701.0000.0000.0000.7710.3930.2310.0000.211
위도(초)0.0000.0000.6880.8170.0001.0000.0000.4340.3250.0000.1250.187
경도(도)0.0000.0001.0001.0000.0000.0001.0001.0000.2030.0000.0000.000
경도(분)0.8940.8940.9980.9860.7710.4341.0001.0000.3920.2110.1050.176
경도(초)0.4440.4440.7870.9420.3930.3250.2030.3921.0000.0000.0000.320
취약지역유형0.2220.2220.6840.5470.2310.0000.0000.2110.0001.0000.0000.062
지정면적(제곱미터)0.0000.0000.4751.0000.0000.1250.0000.1050.0000.0001.0000.628
거리(미터)0.6930.6930.8261.0000.2110.1870.0000.1760.3200.0620.6281.000
2023-12-13T06:01:49.354173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
취약지역유형경도(도)관할조사지(시군구)
취약지역유형1.0000.0000.2670.267
경도(도)0.0001.0000.0000.000
관할0.2670.0001.0001.000
조사지(시군구)0.2670.0001.0001.000
2023-12-13T06:01:49.468395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도(분)위도(초)경도(분)경도(초)지정면적(제곱미터)거리(미터)관할조사지(시군구)경도(도)취약지역유형
위도(분)1.000-0.086-0.3480.1250.050-0.4230.4310.4310.0000.167
위도(초)-0.0861.000-0.112-0.0080.1990.1120.0000.0000.0000.000
경도(분)-0.348-0.1121.000-0.217-0.3050.4280.5560.5560.9840.253
경도(초)0.125-0.008-0.2171.000-0.064-0.1430.1920.1920.1460.000
지정면적(제곱미터)0.0500.199-0.305-0.0641.0000.1910.0000.0000.0000.000
거리(미터)-0.4230.1120.428-0.1430.1911.0000.4810.4810.0000.052
관할0.4310.0000.5560.1920.0000.4811.0001.0000.0000.267
조사지(시군구)0.4310.0000.5560.1920.0000.4811.0001.0000.0000.267
경도(도)0.0000.0000.9840.1460.0000.0000.0000.0001.0000.000
취약지역유형0.1670.0000.2530.0000.0000.0520.2670.2670.0001.000

Missing values

2023-12-13T06:01:44.563775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:01:44.741045image/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

관할조사지(시도)조사지(시군구)조사지(동)지번기타지번위도(도)위도(분)위도(초)경도(도)경도(분)경도(초)취약지역유형지정면적(제곱미터)거리(미터)
0광주광역시 북구광주광역시북구신안동310-6대<NA>351022.51265344.626토석류350.00
1광주광역시 동구광주광역시동구월남동609-2대<NA>35618.881265533.095토석류3836.0700
2광주광역시 동구광주광역시동구용산동산1-2<NA>35732.1781265546.029산사태583.0300
3광주광역시 광산구광주광역시광산구박호동산156-1임<NA>35953.8791264431.411토석류5356.025
4광주광역시 북구광주광역시북구각화동산20-3임<NA>351130.0991265622.1토석류915.030
5광주광역시 북구광주광역시북구각화동산71임<NA>35113.6991265638.5토석류0.00
6광주광역시 북구광주광역시북구충효동산24임<NA>351019.449127056.583토석류1080.00
7광주광역시 북구광주광역시북구망월동산14임<NA>351232.199126579.9토석류623.333350
8광주광역시 북구광주광역시북구금곡동994답<NA>35103.121265839.729토석류1235.40
9광주광역시 북구광주광역시북구장등동산287임<NA>35125.499126569.3토석류0.00
관할조사지(시도)조사지(시군구)조사지(동)지번기타지번위도(도)위도(분)위도(초)경도(도)경도(분)경도(초)취약지역유형지정면적(제곱미터)거리(미터)
86광주광역시 서구광주광역시서구세하동산83<NA>35654.398126500.938토석류1478.231000
87광주광역시 서구광주광역시서구용두동산83-1<NA>35540.212126499.905토석류1445.621000
88광주광역시 북구광주광역시북구장등동산3-1임<NA>351244.91265628.1산사태0.00
89광주광역시 북구광주광역시북구양산동산99-1임<NA>351135.7561265233.819산사태0.00
90광주광역시 광산구광주광역시광산구서봉동산84-1임<NA>35917.51264455.6산사태298.260
91광주광역시 남구광주광역시남구덕남동산 80번지<NA>3559.241265414.12산사태353.022
92광주광역시 북구광주광역시북구두암동산82임<NA>351013.0991265631.1토석류1532.020
93광주광역시 북구광주광역시북구두암동산95-2임<NA>35108.2631265625.757산사태0.00
94광주광역시 북구광주광역시북구효령동880-10구<NA>351418.5551265439.057토석류1239.52220
95광주광역시 동구광주광역시동구지산동산24-3임<NA>3590.4821265634.417산사태249.0100