Overview

Dataset statistics

Number of variables15
Number of observations5396
Missing cells212
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory674.6 KiB
Average record size in memory128.0 B

Variable types

Numeric8
Categorical3
Text3
DateTime1

Dataset

Description경상북도 22개 시군의 산사태취약지역지정현황에 대한 자료를 제공합니다.데이터 기준일자를 참고해 주시기 바랍니다.
Author경상북도
URLhttps://www.data.go.kr/data/15126579/fileData.do

Alerts

시도 has constant value ""Constant
데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 시군구High correlation
(위도)도 is highly overall correlated with (위도)분High correlation
(위도)분 is highly overall correlated with (위도)도High correlation
시군구 is highly overall correlated with 연번High correlation
has 211 (3.9%) missing valuesMissing
(위도)도 is highly skewed (γ1 = 28.39063718)Skewed
(경도)도 is highly skewed (γ1 = -23.43255832)Skewed
연번 has unique valuesUnique
(위도)분 has 122 (2.3%) zerosZeros
(경도)분 has 129 (2.4%) zerosZeros
지정면적(제곱미터) has 948 (17.6%) zerosZeros

Reproduction

Analysis started2024-04-17 18:24:32.141559
Analysis finished2024-04-17 18:24:38.374713
Duration6.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5396
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2698.5
Minimum1
Maximum5396
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.6 KiB
2024-04-18T03:24:38.431261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile270.75
Q11349.75
median2698.5
Q34047.25
95-th percentile5126.25
Maximum5396
Range5395
Interquartile range (IQR)2697.5

Descriptive statistics

Standard deviation1557.8354
Coefficient of variation (CV)0.57729678
Kurtosis-1.2
Mean2698.5
Median Absolute Deviation (MAD)1349
Skewness0
Sum14561106
Variance2426851
MonotonicityStrictly increasing
2024-04-18T03:24:38.546593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
3597 1
 
< 0.1%
3605 1
 
< 0.1%
3604 1
 
< 0.1%
3603 1
 
< 0.1%
3602 1
 
< 0.1%
3601 1
 
< 0.1%
3600 1
 
< 0.1%
3599 1
 
< 0.1%
3598 1
 
< 0.1%
Other values (5386) 5386
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
5396 1
< 0.1%
5395 1
< 0.1%
5394 1
< 0.1%
5393 1
< 0.1%
5392 1
< 0.1%
5391 1
< 0.1%
5390 1
< 0.1%
5389 1
< 0.1%
5388 1
< 0.1%
5387 1
< 0.1%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.3 KiB
경상북도
5396 

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 5396
100.0%

Length

2024-04-18T03:24:38.639884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:24:38.705238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 5396
100.0%

시군구
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size42.3 KiB
경주시
517 
상주시
404 
문경시
363 
안동시
355 
김천시
 
317
Other values (17)
3440 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경주시 517
 
9.6%
상주시 404
 
7.5%
문경시 363
 
6.7%
안동시 355
 
6.6%
김천시 317
 
5.9%
포항시 284
 
5.3%
의성군 280
 
5.2%
성주군 263
 
4.9%
영덕군 243
 
4.5%
청도군 238
 
4.4%
Other values (12) 2132
39.5%

Length

2024-04-18T03:24:38.774332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경주시 517
 
9.6%
상주시 404
 
7.5%
문경시 363
 
6.7%
안동시 355
 
6.6%
김천시 317
 
5.9%
포항시 284
 
5.3%
의성군 280
 
5.2%
성주군 263
 
4.9%
영덕군 243
 
4.5%
청도군 238
 
4.4%
Other values (12) 2132
39.5%
Distinct293
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size42.3 KiB
2024-04-18T03:24:39.040431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9625649
Min length2

Characters and Unicode

Total characters15986
Distinct characters168
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

Unique41 ?
Unique (%)0.8%

Sample

1st row남구
2nd row남구
3rd row남구
4th row남구
5th row남구
ValueCountFrequency (%)
북구 197
 
3.7%
문무대왕면 94
 
1.7%
문경읍 91
 
1.7%
남구 87
 
1.6%
남천면 67
 
1.2%
산내면 63
 
1.2%
산북면 63
 
1.2%
예안면 61
 
1.1%
화북면 60
 
1.1%
북면 57
 
1.1%
Other values (283) 4556
84.4%
2024-04-18T03:24:39.416467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4180
26.1%
721
 
4.5%
545
 
3.4%
535
 
3.3%
493
 
3.1%
458
 
2.9%
403
 
2.5%
362
 
2.3%
286
 
1.8%
265
 
1.7%
Other values (158) 7738
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15986
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4180
26.1%
721
 
4.5%
545
 
3.4%
535
 
3.3%
493
 
3.1%
458
 
2.9%
403
 
2.5%
362
 
2.3%
286
 
1.8%
265
 
1.7%
Other values (158) 7738
48.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15986
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4180
26.1%
721
 
4.5%
545
 
3.4%
535
 
3.3%
493
 
3.1%
458
 
2.9%
403
 
2.5%
362
 
2.3%
286
 
1.8%
265
 
1.7%
Other values (158) 7738
48.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15986
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
4180
26.1%
721
 
4.5%
545
 
3.4%
535
 
3.3%
493
 
3.1%
458
 
2.9%
403
 
2.5%
362
 
2.3%
286
 
1.8%
265
 
1.7%
Other values (158) 7738
48.4%


Text

MISSING 

Distinct1235
Distinct (%)23.8%
Missing211
Missing (%)3.9%
Memory size42.3 KiB
2024-04-18T03:24:39.686567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9737705
Min length2

Characters and Unicode

Total characters15419
Distinct characters260
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

Unique331 ?
Unique (%)6.4%

Sample

1st row구룡포읍
2nd row구룡포읍
3rd row구룡포읍
4th row구룡포읍
5th row구룡포읍
ValueCountFrequency (%)
죽장면 62
 
1.2%
대곡리 33
 
0.6%
흥해읍 33
 
0.6%
금곡리 30
 
0.6%
기계면 25
 
0.5%
신곡리 24
 
0.5%
도촌리 24
 
0.5%
보현리 24
 
0.5%
덕산리 22
 
0.4%
용동리 22
 
0.4%
Other values (1225) 4886
94.2%
2024-04-18T03:24:40.297100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4951
32.1%
460
 
3.0%
355
 
2.3%
275
 
1.8%
265
 
1.7%
214
 
1.4%
201
 
1.3%
196
 
1.3%
186
 
1.2%
183
 
1.2%
Other values (250) 8133
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15419
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4951
32.1%
460
 
3.0%
355
 
2.3%
275
 
1.8%
265
 
1.7%
214
 
1.4%
201
 
1.3%
196
 
1.3%
186
 
1.2%
183
 
1.2%
Other values (250) 8133
52.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15419
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4951
32.1%
460
 
3.0%
355
 
2.3%
275
 
1.8%
265
 
1.7%
214
 
1.4%
201
 
1.3%
196
 
1.3%
186
 
1.2%
183
 
1.2%
Other values (250) 8133
52.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15419
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
4951
32.1%
460
 
3.0%
355
 
2.3%
275
 
1.8%
265
 
1.7%
214
 
1.4%
201
 
1.3%
196
 
1.3%
186
 
1.2%
183
 
1.2%
Other values (250) 8133
52.7%

지번
Text

Distinct2940
Distinct (%)54.5%
Missing1
Missing (%)< 0.1%
Memory size42.3 KiB
2024-04-18T03:24:40.555617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length25
Mean length5.2947173
Min length2

Characters and Unicode

Total characters28565
Distinct characters68
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2250 ?
Unique (%)41.7%

Sample

1st row산109-11임
2nd row산74-20임
3rd row산93-16임
4th row산107임
5th row산59임
ValueCountFrequency (%)
124
 
2.0%
111
 
1.8%
안동 75
 
1.2%
산13임 24
 
0.4%
산20임 22
 
0.4%
산12임 22
 
0.4%
산8임 21
 
0.3%
21
 
0.3%
산18임 21
 
0.3%
산33임 20
 
0.3%
Other values (2868) 5632
92.4%
2024-04-18T03:24:40.912818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4431
15.5%
1 4013
14.0%
3160
11.1%
2 2065
 
7.2%
- 1916
 
6.7%
3 1620
 
5.7%
4 1358
 
4.8%
5 1333
 
4.7%
6 1307
 
4.6%
7 1142
 
4.0%
Other values (58) 6220
21.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16076
56.3%
Other Letter 9659
33.8%
Dash Punctuation 1916
 
6.7%
Space Separator 709
 
2.5%
Other Punctuation 127
 
0.4%
Close Punctuation 30
 
0.1%
Open Punctuation 30
 
0.1%
Lowercase Letter 12
 
< 0.1%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4431
45.9%
3160
32.7%
665
 
6.9%
242
 
2.5%
169
 
1.7%
136
 
1.4%
119
 
1.2%
110
 
1.1%
108
 
1.1%
80
 
0.8%
Other values (34) 439
 
4.5%
Decimal Number
ValueCountFrequency (%)
1 4013
25.0%
2 2065
12.8%
3 1620
10.1%
4 1358
 
8.4%
5 1333
 
8.3%
6 1307
 
8.1%
7 1142
 
7.1%
8 1095
 
6.8%
9 1073
 
6.7%
0 1070
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
a 4
33.3%
n 2
16.7%
e 2
16.7%
b 2
16.7%
y 1
 
8.3%
r 1
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
J 2
33.3%
M 2
33.3%
F 2
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 1916
100.0%
Space Separator
ValueCountFrequency (%)
709
100.0%
Other Punctuation
ValueCountFrequency (%)
, 127
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18888
66.1%
Hangul 9659
33.8%
Latin 18
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4431
45.9%
3160
32.7%
665
 
6.9%
242
 
2.5%
169
 
1.7%
136
 
1.4%
119
 
1.2%
110
 
1.1%
108
 
1.1%
80
 
0.8%
Other values (34) 439
 
4.5%
Common
ValueCountFrequency (%)
1 4013
21.2%
2 2065
10.9%
- 1916
10.1%
3 1620
8.6%
4 1358
 
7.2%
5 1333
 
7.1%
6 1307
 
6.9%
7 1142
 
6.0%
8 1095
 
5.8%
9 1073
 
5.7%
Other values (5) 1966
10.4%
Latin
ValueCountFrequency (%)
a 4
22.2%
J 2
11.1%
n 2
11.1%
e 2
11.1%
M 2
11.1%
b 2
11.1%
F 2
11.1%
y 1
 
5.6%
r 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18906
66.2%
Hangul 9658
33.8%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
4431
45.9%
3160
32.7%
665
 
6.9%
242
 
2.5%
169
 
1.7%
136
 
1.4%
119
 
1.2%
110
 
1.1%
108
 
1.1%
80
 
0.8%
Other values (33) 438
 
4.5%
ASCII
ValueCountFrequency (%)
1 4013
21.2%
2 2065
10.9%
- 1916
10.1%
3 1620
8.6%
4 1358
 
7.2%
5 1333
 
7.1%
6 1307
 
6.9%
7 1142
 
6.0%
8 1095
 
5.8%
9 1073
 
5.7%
Other values (14) 1984
10.5%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

(위도)도
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.724981
Minimum1
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.6 KiB
2024-04-18T03:24:41.011743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q135
median36
Q336
95-th percentile36
Maximum128
Range127
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6147991
Coefficient of variation (CV)0.045200837
Kurtosis2104.8466
Mean35.724981
Median Absolute Deviation (MAD)0
Skewness28.390637
Sum192772
Variance2.607576
MonotonicityNot monotonic
2024-04-18T03:24:41.092061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
36 3688
68.3%
35 1572
29.1%
37 128
 
2.4%
1 3
 
0.1%
34 2
 
< 0.1%
11 1
 
< 0.1%
128 1
 
< 0.1%
38 1
 
< 0.1%
ValueCountFrequency (%)
1 3
 
0.1%
11 1
 
< 0.1%
34 2
 
< 0.1%
35 1572
29.1%
36 3688
68.3%
37 128
 
2.4%
38 1
 
< 0.1%
128 1
 
< 0.1%
ValueCountFrequency (%)
128 1
 
< 0.1%
38 1
 
< 0.1%
37 128
 
2.4%
36 3688
68.3%
35 1572
29.1%
34 2
 
< 0.1%
11 1
 
< 0.1%
1 3
 
0.1%

(위도)분
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.228503
Minimum0
Maximum59
Zeros122
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size47.6 KiB
2024-04-18T03:24:41.185737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q119
median38
Q347
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)28

Descriptive statistics

Standard deviation17.688549
Coefficient of variation (CV)0.5323306
Kurtosis-1.0789206
Mean33.228503
Median Absolute Deviation (MAD)13
Skewness-0.40771418
Sum179301
Variance312.88476
MonotonicityNot monotonic
2024-04-18T03:24:41.287737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 182
 
3.4%
43 173
 
3.2%
44 170
 
3.2%
45 170
 
3.2%
55 164
 
3.0%
46 147
 
2.7%
42 136
 
2.5%
41 132
 
2.4%
39 128
 
2.4%
56 126
 
2.3%
Other values (50) 3868
71.7%
ValueCountFrequency (%)
0 122
2.3%
1 105
1.9%
2 98
1.8%
3 89
1.6%
4 78
1.4%
5 85
1.6%
6 60
1.1%
7 59
1.1%
8 66
1.2%
9 47
 
0.9%
ValueCountFrequency (%)
59 98
1.8%
58 110
2.0%
57 117
2.2%
56 126
2.3%
55 164
3.0%
54 93
1.7%
53 80
1.5%
52 104
1.9%
51 103
1.9%
50 107
2.0%

(위도)초
Real number (ℝ)

Distinct3375
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.017284
Minimum0
Maximum59.999
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size47.6 KiB
2024-04-18T03:24:41.394724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.999
Q115.22
median30.2725
Q344.799
95-th percentile56.806
Maximum59.999
Range59.999
Interquartile range (IQR)29.579

Descriptive statistics

Standard deviation17.215214
Coefficient of variation (CV)0.57351004
Kurtosis-1.190576
Mean30.017284
Median Absolute Deviation (MAD)14.7275
Skewness-0.0053500998
Sum161973.27
Variance296.3636
MonotonicityNot monotonic
2024-04-18T03:24:41.500398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.999 10
 
0.2%
24.999 10
 
0.2%
40.999 10
 
0.2%
7.499 10
 
0.2%
4.999 10
 
0.2%
37.999 10
 
0.2%
12.999 9
 
0.2%
44.999 9
 
0.2%
18.999 9
 
0.2%
27.099 9
 
0.2%
Other values (3365) 5300
98.2%
ValueCountFrequency (%)
0.0 5
0.1%
0.006 1
 
< 0.1%
0.044 1
 
< 0.1%
0.054 1
 
< 0.1%
0.099 4
0.1%
0.1 2
 
< 0.1%
0.156 1
 
< 0.1%
0.179 1
 
< 0.1%
0.185 1
 
< 0.1%
0.199 2
 
< 0.1%
ValueCountFrequency (%)
59.999 4
0.1%
59.994 1
 
< 0.1%
59.976 1
 
< 0.1%
59.974 1
 
< 0.1%
59.925 1
 
< 0.1%
59.9 1
 
< 0.1%
59.899 6
0.1%
59.897 1
 
< 0.1%
59.818 1
 
< 0.1%
59.786 1
 
< 0.1%

(경도)도
Real number (ℝ)

SKEWED 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.09377
Minimum1
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.6 KiB
2024-04-18T03:24:41.590940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile128
Q1128
median128
Q3129
95-th percentile129
Maximum130
Range129
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.6374898
Coefficient of variation (CV)0.036203866
Kurtosis567.25352
Mean128.09377
Median Absolute Deviation (MAD)0
Skewness-23.432558
Sum691194
Variance21.506312
MonotonicityNot monotonic
2024-04-18T03:24:41.680958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
128 3411
63.2%
129 1704
31.6%
127 228
 
4.2%
130 43
 
0.8%
35 6
 
0.1%
1 3
 
0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
1 3
 
0.1%
11 1
 
< 0.1%
35 6
 
0.1%
127 228
 
4.2%
128 3411
63.2%
129 1704
31.6%
130 43
 
0.8%
ValueCountFrequency (%)
130 43
 
0.8%
129 1704
31.6%
128 3411
63.2%
127 228
 
4.2%
35 6
 
0.1%
11 1
 
< 0.1%
1 3
 
0.1%

(경도)분
Real number (ℝ)

ZEROS 

Distinct60
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.649741
Minimum0
Maximum59
Zeros129
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size47.6 KiB
2024-04-18T03:24:41.774677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median21
Q343
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)33

Descriptive statistics

Standard deviation18.15591
Coefficient of variation (CV)0.70783991
Kurtosis-1.1617781
Mean25.649741
Median Absolute Deviation (MAD)14
Skewness0.39069537
Sum138406
Variance329.63707
MonotonicityNot monotonic
2024-04-18T03:24:41.883824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 159
 
2.9%
7 139
 
2.6%
21 136
 
2.5%
19 135
 
2.5%
8 133
 
2.5%
6 132
 
2.4%
5 131
 
2.4%
4 130
 
2.4%
3 130
 
2.4%
18 130
 
2.4%
Other values (50) 4041
74.9%
ValueCountFrequency (%)
0 129
2.4%
1 125
2.3%
2 124
2.3%
3 130
2.4%
4 130
2.4%
5 131
2.4%
6 132
2.4%
7 139
2.6%
8 133
2.5%
9 104
1.9%
ValueCountFrequency (%)
59 110
2.0%
58 80
1.5%
57 117
2.2%
56 73
1.4%
55 97
1.8%
54 101
1.9%
53 80
1.5%
52 82
1.5%
51 62
1.1%
50 59
1.1%

(경도)초
Real number (ℝ)

Distinct3075
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.024905
Minimum0
Maximum59.999
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size47.6 KiB
2024-04-18T03:24:42.015306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.1085
Q115.33575
median29.965
Q344.71925
95-th percentile57.0445
Maximum59.999
Range59.999
Interquartile range (IQR)29.3835

Descriptive statistics

Standard deviation17.104609
Coefficient of variation (CV)0.56968071
Kurtosis-1.1688661
Mean30.024905
Median Absolute Deviation (MAD)14.7345
Skewness0.01381863
Sum162014.39
Variance292.56766
MonotonicityNot monotonic
2024-04-18T03:24:42.142292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.0 21
 
0.4%
18.0 19
 
0.4%
24.0 19
 
0.4%
27.0 17
 
0.3%
15.0 17
 
0.3%
59.0 15
 
0.3%
23.0 15
 
0.3%
44.0 14
 
0.3%
30.0 14
 
0.3%
26.0 14
 
0.3%
Other values (3065) 5231
96.9%
ValueCountFrequency (%)
0.0 6
0.1%
0.011 1
 
< 0.1%
0.013 1
 
< 0.1%
0.04 1
 
< 0.1%
0.042 1
 
< 0.1%
0.043 1
 
< 0.1%
0.069 1
 
< 0.1%
0.087 1
 
< 0.1%
0.088 1
 
< 0.1%
0.1 2
 
< 0.1%
ValueCountFrequency (%)
59.999 2
 
< 0.1%
59.995 1
 
< 0.1%
59.9 2
 
< 0.1%
59.895 1
 
< 0.1%
59.8 5
0.1%
59.764 1
 
< 0.1%
59.76 1
 
< 0.1%
59.728 1
 
< 0.1%
59.702 1
 
< 0.1%
59.7 3
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.3 KiB
토석류
4623 
산사태
773 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
토석류 4623
85.7%
산사태 773
 
14.3%

Length

2024-04-18T03:24:42.266933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:24:42.339366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
토석류 4623
85.7%
산사태 773
 
14.3%

지정면적(제곱미터)
Real number (ℝ)

ZEROS 

Distinct3078
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14454.454
Minimum0
Maximum2365000
Zeros948
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size47.6 KiB
2024-04-18T03:24:42.423186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1335
median1250
Q32866
95-th percentile40198.5
Maximum2365000
Range2365000
Interquartile range (IQR)2531

Descriptive statistics

Standard deviation97884.798
Coefficient of variation (CV)6.771947
Kurtosis266.00949
Mean14454.454
Median Absolute Deviation (MAD)1127
Skewness14.72064
Sum77996235
Variance9.5814337 × 109
MonotonicityNot monotonic
2024-04-18T03:24:42.529624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 948
 
17.6%
1250.0 116
 
2.1%
1249.0 69
 
1.3%
1000.0 52
 
1.0%
10000.0 34
 
0.6%
500.0 23
 
0.4%
3000.0 21
 
0.4%
703.0 18
 
0.3%
2000.0 17
 
0.3%
5000.0 12
 
0.2%
Other values (3068) 4086
75.7%
ValueCountFrequency (%)
0.0 948
17.6%
0.052 1
 
< 0.1%
0.3 1
 
< 0.1%
3.0 1
 
< 0.1%
11.4 1
 
< 0.1%
21.0 1
 
< 0.1%
27.0 1
 
< 0.1%
28.0 1
 
< 0.1%
31.0 1
 
< 0.1%
32.0 1
 
< 0.1%
ValueCountFrequency (%)
2365000.0 1
< 0.1%
2126000.0 1
< 0.1%
2069617.0 1
< 0.1%
1980918.0 1
< 0.1%
1895000.0 1
< 0.1%
1842000.0 1
< 0.1%
1696760.0 1
< 0.1%
1347519.0 1
< 0.1%
1343901.0 1
< 0.1%
1333465.0 1
< 0.1%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.3 KiB
Minimum2023-12-31 00:00:00
Maximum2023-12-31 00:00:00
2024-04-18T03:24:42.611002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:42.676820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-18T03:24:37.524027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:33.313018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:33.869740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:34.432979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:35.213478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:35.819855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:36.388344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:36.967574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:37.589060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:33.378488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:33.935674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:34.500913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:35.281265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:35.887150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:36.459292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:37.036863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:37.659453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:33.457691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:34.003009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:34.568666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:35.351594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:35.956839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:36.527442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:37.106580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:37.728600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:33.526516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:34.076072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:34.638202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:35.420742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:36.026889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:36.598350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:37.177279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:37.796935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:33.595456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:34.146717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:34.709215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:35.509614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:36.105368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:36.673970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:37.246866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:37.863480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:33.663397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:34.222383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:34.781737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:35.600313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:36.174365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:36.751149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:37.314686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:37.930919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:33.732533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:34.291709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:35.072564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:35.679587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:36.249674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:36.825031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:37.386392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:38.000664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:33.800418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:34.367115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:35.144748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:35.752337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:36.319228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:36.899268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:24:37.455868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T03:24:42.735827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군구(위도)도(위도)분(위도)초(경도)도(경도)분(경도)초취약지역유형지정면적(제곱미터)
연번1.0000.9830.0840.6950.0000.1050.6830.0320.1230.060
시군구0.9831.0000.1770.8310.0870.1940.7960.0500.1740.156
(위도)도0.0840.1771.0000.0860.0860.9630.0830.0900.0000.000
(위도)분0.6950.8310.0861.0000.0000.1010.3020.0000.0770.075
(위도)초0.0000.0870.0860.0001.0000.0780.0620.0360.0000.000
(경도)도0.1050.1940.9630.1010.0781.0000.0890.1010.0000.000
(경도)분0.6830.7960.0830.3020.0620.0891.0000.0820.1210.037
(경도)초0.0320.0500.0900.0000.0360.1010.0821.0000.0000.034
취약지역유형0.1230.1740.0000.0770.0000.0000.1210.0001.0000.024
지정면적(제곱미터)0.0600.1560.0000.0750.0000.0000.0370.0340.0241.000
2024-04-18T03:24:42.823716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구취약지역유형
시군구1.0000.138
취약지역유형0.1381.000
2024-04-18T03:24:42.892295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번(위도)도(위도)분(위도)초(경도)도(경도)분(경도)초지정면적(제곱미터)시군구취약지역유형
연번1.0000.1400.111-0.014-0.1020.1210.0380.1140.9000.094
(위도)도0.1401.000-0.5950.0140.0000.0020.0070.1640.0920.000
(위도)분0.111-0.5951.000-0.0250.0100.0500.025-0.1450.4940.059
(위도)초-0.0140.014-0.0251.000-0.0160.015-0.009-0.0050.0320.000
(경도)도-0.1020.0000.010-0.0161.000-0.428-0.0250.0060.1020.000
(경도)분0.1210.0020.0500.015-0.4281.0000.0380.0090.4460.092
(경도)초0.0380.0070.025-0.009-0.0250.0381.000-0.0080.0180.000
지정면적(제곱미터)0.1140.164-0.145-0.0050.0060.009-0.0081.0000.0610.024
시군구0.9000.0920.4940.0320.1020.4460.0180.0611.0000.138
취약지역유형0.0940.0000.0590.0000.0000.0920.0000.0240.1381.000

Missing values

2024-04-18T03:24:38.096172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T03:24:38.239774image/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.
2024-04-18T03:24:38.336482image/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

연번시도시군구읍면동지번(위도)도(위도)분(위도)초(경도)도(경도)분(경도)초취약지역유형지정면적(제곱미터)데이터기준일자
01경상북도포항시남구구룡포읍산109-11임355949.9271293246.592토석류2887.02023-12-31
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