Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory97.3 B

Variable types

Numeric6
Text1
Categorical4

Alerts

시도코드 has constant value ""Constant
시도명 has constant value ""Constant
시군구코드 is highly overall correlated with 시군구명High correlation
시군구명 is highly overall correlated with 시군구코드High correlation
아이디 is highly overall correlated with y절편High correlation
기울기 is highly overall correlated with y절편 and 2 other fieldsHigh correlation
y절편 is highly overall correlated with 아이디 and 3 other fieldsHigh correlation
20cm 침수심 유발 강우량 is highly overall correlated with 기울기 and 2 other fieldsHigh correlation
50cm 침수심 유발 강우량 is highly overall correlated with 기울기 and 2 other fieldsHigh correlation
아이디 has unique valuesUnique
격자번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 11:12:56.535125
Analysis finished2023-12-10 11:13:01.278673
Duration4.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아이디
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35808.77
Minimum35228
Maximum36222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:13:01.360302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35228
5-th percentile35324.95
Q135598.75
median35823.5
Q336023.25
95-th percentile36217.05
Maximum36222
Range994
Interquartile range (IQR)424.5

Descriptive statistics

Standard deviation284.25337
Coefficient of variation (CV)0.0079380936
Kurtosis-0.96605788
Mean35808.77
Median Absolute Deviation (MAD)201
Skewness-0.28665055
Sum3580877
Variance80799.977
MonotonicityStrictly increasing
2023-12-10T20:13:01.523034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35228 1
 
1.0%
35924 1
 
1.0%
36023 1
 
1.0%
36022 1
 
1.0%
36021 1
 
1.0%
36020 1
 
1.0%
36019 1
 
1.0%
36018 1
 
1.0%
36017 1
 
1.0%
36016 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
35228 1
1.0%
35229 1
1.0%
35230 1
1.0%
35323 1
1.0%
35324 1
1.0%
35325 1
1.0%
35326 1
1.0%
35327 1
1.0%
35421 1
1.0%
35422 1
1.0%
ValueCountFrequency (%)
36222 1
1.0%
36221 1
1.0%
36220 1
1.0%
36219 1
1.0%
36218 1
1.0%
36217 1
1.0%
36216 1
1.0%
36215 1
1.0%
36214 1
1.0%
36213 1
1.0%

격자번호
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:13:01.907624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row다사3550
2nd row다사3551
3rd row다사3552
4th row다사3649
5th row다사3650
ValueCountFrequency (%)
다사3550 1
 
1.0%
다사4249 1
 
1.0%
다사4349 1
 
1.0%
다사4348 1
 
1.0%
다사4347 1
 
1.0%
다사4346 1
 
1.0%
다사4345 1
 
1.0%
다사4344 1
 
1.0%
다사4343 1
 
1.0%
다사4253 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T20:13:02.403241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 143
23.8%
다 100
16.7%
사 100
16.7%
5 69
11.5%
3 55
 
9.2%
2 26
 
4.3%
1 25
 
4.2%
0 21
 
3.5%
9 18
 
3.0%
8 17
 
2.8%
Other values (2) 26
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
66.7%
Other Letter 200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 143
35.8%
5 69
17.2%
3 55
 
13.8%
2 26
 
6.5%
1 25
 
6.2%
0 21
 
5.2%
9 18
 
4.5%
8 17
 
4.2%
7 14
 
3.5%
6 12
 
3.0%
Other Letter
ValueCountFrequency (%)
다 100
50.0%
사 100
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 400
66.7%
Hangul 200
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
4 143
35.8%
5 69
17.2%
3 55
 
13.8%
2 26
 
6.5%
1 25
 
6.2%
0 21
 
5.2%
9 18
 
4.5%
8 17
 
4.2%
7 14
 
3.5%
6 12
 
3.0%
Hangul
ValueCountFrequency (%)
다 100
50.0%
사 100
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
66.7%
Hangul 200
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 143
35.8%
5 69
17.2%
3 55
 
13.8%
2 26
 
6.5%
1 25
 
6.2%
0 21
 
5.2%
9 18
 
4.5%
8 17
 
4.2%
7 14
 
3.5%
6 12
 
3.0%
Hangul
ValueCountFrequency (%)
다 100
50.0%
사 100
50.0%

시도코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
11
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 100
100.0%

Length

2023-12-10T20:13:02.584509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:02.691555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 100
100.0%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 100
100.0%

Length

2023-12-10T20:13:02.819779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:02.932496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 100
100.0%

시군구코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
11500
63 
11470
17 
11530
11 
11545
8 
11440
 
1

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row11500
2nd row11500
3rd row11500
4th row11500
5th row11500

Common Values

ValueCountFrequency (%)
11500 63
63.0%
11470 17
 
17.0%
11530 11
 
11.0%
11545 8
 
8.0%
11440 1
 
1.0%

Length

2023-12-10T20:13:03.069486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:03.201142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11500 63
63.0%
11470 17
 
17.0%
11530 11
 
11.0%
11545 8
 
8.0%
11440 1
 
1.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강서구
63 
양천구
17 
구로구
11 
금천구
8 
마포구
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
강서구 63
63.0%
양천구 17
 
17.0%
구로구 11
 
11.0%
금천구 8
 
8.0%
마포구 1
 
1.0%

Length

2023-12-10T20:13:03.353431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:03.502803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강서구 63
63.0%
양천구 17
 
17.0%
구로구 11
 
11.0%
금천구 8
 
8.0%
마포구 1
 
1.0%

기울기
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.106
Minimum3.02
Maximum45.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:13:03.633350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.02
5-th percentile7.61
Q110.02
median12.78
Q317
95-th percentile29.25
Maximum45.23
Range42.21
Interquartile range (IQR)6.98

Descriptive statistics

Standard deviation8.798475
Coefficient of variation (CV)0.58244902
Kurtosis0.24208385
Mean15.106
Median Absolute Deviation (MAD)2.76
Skewness1.1716488
Sum1510.6
Variance77.413162
MonotonicityNot monotonic
2023-12-10T20:13:03.765789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
12.78 27
27.0%
10.02 27
27.0%
29.25 23
23.0%
7.61 18
18.0%
3.02 2
 
2.0%
17.0 2
 
2.0%
45.23 1
 
1.0%
ValueCountFrequency (%)
3.02 2
 
2.0%
7.61 18
18.0%
10.02 27
27.0%
12.78 27
27.0%
17.0 2
 
2.0%
29.25 23
23.0%
45.23 1
 
1.0%
ValueCountFrequency (%)
45.23 1
 
1.0%
29.25 23
23.0%
17.0 2
 
2.0%
12.78 27
27.0%
10.02 27
27.0%
7.61 18
18.0%
3.02 2
 
2.0%

y절편
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-87.3905
Minimum-412.6
Maximum-15.22
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)100.0%
Memory size1.0 KiB
2023-12-10T20:13:03.898618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-412.6
5-th percentile-235.7
Q1-61.22
median-48.88
Q3-40.47
95-th percentile-18.89
Maximum-15.22
Range397.38
Interquartile range (IQR)20.75

Descriptive statistics

Standard deviation90.307844
Coefficient of variation (CV)-1.0333829
Kurtosis0.61081648
Mean-87.3905
Median Absolute Deviation (MAD)8.41
Skewness-1.3787553
Sum-8739.05
Variance8155.5067
MonotonicityNot monotonic
2023-12-10T20:13:04.052881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-40.47 27
27.0%
-48.88 27
27.0%
-235.7 23
23.0%
-18.89 18
18.0%
-15.22 2
 
2.0%
-61.22 2
 
2.0%
-412.6 1
 
1.0%
ValueCountFrequency (%)
-412.6 1
 
1.0%
-235.7 23
23.0%
-61.22 2
 
2.0%
-48.88 27
27.0%
-40.47 27
27.0%
-18.89 18
18.0%
-15.22 2
 
2.0%
ValueCountFrequency (%)
-15.22 2
 
2.0%
-18.89 18
18.0%
-40.47 27
27.0%
-48.88 27
27.0%
-61.22 2
 
2.0%
-235.7 23
23.0%
-412.6 1
 
1.0%

10cm 침수심 유발 강우량
Real number (ℝ)

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.6679
Minimum14.99
Maximum108.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:13:04.211614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.99
5-th percentile51.32
Q151.32
median56.8
Q387.33
95-th percentile87.33
Maximum108.78
Range93.79
Interquartile range (IQR)36.01

Descriptive statistics

Standard deviation17.539417
Coefficient of variation (CV)0.27548289
Kurtosis0.19754873
Mean63.6679
Median Absolute Deviation (MAD)5.48
Skewness0.44644499
Sum6366.79
Variance307.63114
MonotonicityNot monotonic
2023-12-10T20:13:04.345548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
87.33 27
27.0%
51.32 27
27.0%
56.8 23
23.0%
57.2 18
18.0%
14.99 2
 
2.0%
108.78 2
 
2.0%
39.7 1
 
1.0%
ValueCountFrequency (%)
14.99 2
 
2.0%
39.7 1
 
1.0%
51.32 27
27.0%
56.8 23
23.0%
57.2 18
18.0%
87.33 27
27.0%
108.78 2
 
2.0%
ValueCountFrequency (%)
108.78 2
 
2.0%
87.33 27
27.0%
57.2 18
18.0%
56.8 23
23.0%
51.32 27
27.0%
39.7 1
 
1.0%
14.99 2
 
2.0%

20cm 침수심 유발 강우량
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214.7263
Minimum45.2
Maximum492
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:13:04.479727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45.2
5-th percentile133.29
Q1151.52
median215.13
Q3278.78
95-th percentile349.3
Maximum492
Range446.8
Interquartile range (IQR)127.26

Descriptive statistics

Standard deviation89.122586
Coefficient of variation (CV)0.41505203
Kurtosis-0.30967543
Mean214.7263
Median Absolute Deviation (MAD)63.61
Skewness0.75151814
Sum21472.63
Variance7942.8354
MonotonicityNot monotonic
2023-12-10T20:13:04.606094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
215.13 27
27.0%
151.52 27
27.0%
349.3 23
23.0%
133.29 18
18.0%
45.2 2
 
2.0%
278.78 2
 
2.0%
492.0 1
 
1.0%
ValueCountFrequency (%)
45.2 2
 
2.0%
133.29 18
18.0%
151.52 27
27.0%
215.13 27
27.0%
278.78 2
 
2.0%
349.3 23
23.0%
492.0 1
 
1.0%
ValueCountFrequency (%)
492.0 1
 
1.0%
349.3 23
23.0%
278.78 2
 
2.0%
215.13 27
27.0%
151.52 27
27.0%
133.29 18
18.0%
45.2 2
 
2.0%

50cm 침수심 유발 강우량
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean667.9015
Minimum135.83
Maximum1848.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:13:04.721157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum135.83
5-th percentile361.56
Q1452.12
median598.53
Q3788.78
95-th percentile1226.8
Maximum1848.9
Range1713.07
Interquartile range (IQR)336.66

Descriptive statistics

Standard deviation351.77681
Coefficient of variation (CV)0.52668966
Kurtosis0.10830201
Mean667.9015
Median Absolute Deviation (MAD)146.41
Skewness1.0813777
Sum66790.15
Variance123746.93
MonotonicityNot monotonic
2023-12-10T20:13:04.832778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
598.53 27
27.0%
452.12 27
27.0%
1226.8 23
23.0%
361.56 18
18.0%
135.83 2
 
2.0%
788.78 2
 
2.0%
1848.9 1
 
1.0%
ValueCountFrequency (%)
135.83 2
 
2.0%
361.56 18
18.0%
452.12 27
27.0%
598.53 27
27.0%
788.78 2
 
2.0%
1226.8 23
23.0%
1848.9 1
 
1.0%
ValueCountFrequency (%)
1848.9 1
 
1.0%
1226.8 23
23.0%
788.78 2
 
2.0%
598.53 27
27.0%
452.12 27
27.0%
361.56 18
18.0%
135.83 2
 
2.0%

Interactions

2023-12-10T20:13:00.320811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:56.909181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:57.540048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:58.145182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:59.148142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:59.744032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:00.453169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:57.003014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:57.645176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:58.260160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:59.244381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:59.841545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:00.564628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:57.095203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:57.737373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:58.385463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:59.327828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:59.919200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:00.687132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:57.228629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:57.858119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:58.512395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:59.455908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:00.013547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:00.781550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:57.333447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:57.942899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:58.628632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:59.553671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:00.109263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:00.872690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:57.435120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:58.047759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:58.725245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:12:59.650648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:00.199172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:13:04.940681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디격자번호시군구코드시군구명기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
아이디1.0001.0000.5690.5690.3460.0350.4070.6260.450
격자번호1.0001.0001.0001.0001.0001.0001.0001.0001.000
시군구코드0.5691.0001.0001.0000.3810.2590.5580.6040.633
시군구명0.5691.0001.0001.0000.3810.2590.5580.6040.633
기울기0.3461.0000.3810.3811.0001.0000.9981.0001.000
y절편0.0351.0000.2590.2591.0001.0000.8121.0001.000
10cm 침수심 유발 강우량0.4071.0000.5580.5580.9980.8121.0001.0000.996
20cm 침수심 유발 강우량0.6261.0000.6040.6041.0001.0001.0001.0001.000
50cm 침수심 유발 강우량0.4501.0000.6330.6331.0001.0000.9961.0001.000
2023-12-10T20:13:05.106234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드시군구명
시군구코드1.0001.000
시군구명1.0001.000
2023-12-10T20:13:05.206542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량시군구코드시군구명
아이디1.0000.402-0.637-0.4080.4020.4020.2660.266
기울기0.4021.000-0.7490.1761.0001.0000.2670.267
y절편-0.637-0.7491.0000.455-0.749-0.7490.1230.123
10cm 침수심 유발 강우량-0.4080.1760.4551.0000.1760.1760.4170.417
20cm 침수심 유발 강우량0.4021.000-0.7490.1761.0001.0000.4400.440
50cm 침수심 유발 강우량0.4021.000-0.7490.1761.0001.0000.2670.267
시군구코드0.2660.2670.1230.4170.4400.2671.0001.000
시군구명0.2660.2670.1230.4170.4400.2671.0001.000

Missing values

2023-12-10T20:13:01.008652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:13:01.202138image/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

아이디격자번호시도코드시도명시군구코드시군구명기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
035228다사355011서울11500강서구7.61-18.8957.2133.29361.56
135229다사355111서울11500강서구7.61-18.8957.2133.29361.56
235230다사355211서울11500강서구7.61-18.8957.2133.29361.56
335323다사364911서울11500강서구7.61-18.8957.2133.29361.56
435324다사365011서울11500강서구7.61-18.8957.2133.29361.56
535325다사365111서울11500강서구7.61-18.8957.2133.29361.56
635326다사365211서울11500강서구7.61-18.8957.2133.29361.56
735327다사365311서울11500강서구7.61-18.8957.2133.29361.56
835421다사374811서울11500강서구7.61-18.8957.2133.29361.56
935422다사374911서울11500강서구7.61-18.8957.2133.29361.56
아이디격자번호시도코드시도명시군구코드시군구명기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
9036213다사454011서울11545금천구10.02-48.8851.32151.52452.12
9136214다사454111서울11545금천구10.02-48.8851.32151.52452.12
9236215다사454211서울11545금천구10.02-48.8851.32151.52452.12
9336216다사454311서울11545금천구10.02-48.8851.32151.52452.12
9436217다사454411서울11530구로구10.02-48.8851.32151.52452.12
9536218다사454511서울11530구로구10.02-48.8851.32151.52452.12
9636219다사454611서울11470양천구10.02-48.8851.32151.52452.12
9736220다사454711서울11470양천구29.25-235.756.8349.31226.8
9836221다사454811서울11470양천구29.25-235.756.8349.31226.8
9936222다사454911서울11500강서구45.23-412.639.7492.01848.9