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 has constant value ""Constant
시군구명 has constant value ""Constant
기울기 is highly overall correlated with y절편 and 3 other fieldsHigh correlation
y절편 is highly overall correlated with 기울기 and 3 other fieldsHigh correlation
10cm 침수심 유발 강우량 is highly overall correlated with 기울기 and 3 other fieldsHigh correlation
20cm 침수심 유발 강우량 is highly overall correlated with 기울기 and 3 other fieldsHigh correlation
50cm 침수심 유발 강우량 is highly overall correlated with 기울기 and 3 other fieldsHigh correlation
아이디 has unique valuesUnique
격자번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 11:13:19.290190
Analysis finished2023-12-10 11:13:23.837602
Duration4.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아이디
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57253.53
Minimum56892
Maximum57490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:13:23.925014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56892
5-th percentile56980.55
Q157185.75
median57292.5
Q357392.25
95-th percentile57422.35
Maximum57490
Range598
Interquartile range (IQR)206.5

Descriptive statistics

Standard deviation156.64787
Coefficient of variation (CV)0.0027360386
Kurtosis-0.36212545
Mean57253.53
Median Absolute Deviation (MAD)103
Skewness-0.66301692
Sum5725353
Variance24538.555
MonotonicityStrictly increasing
2023-12-10T20:13:24.110857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56892 1
 
1.0%
57315 1
 
1.0%
57392 1
 
1.0%
57391 1
 
1.0%
57390 1
 
1.0%
57389 1
 
1.0%
57388 1
 
1.0%
57387 1
 
1.0%
57386 1
 
1.0%
57385 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
56892 1
1.0%
56893 1
1.0%
56894 1
1.0%
56895 1
1.0%
56896 1
1.0%
56985 1
1.0%
56991 1
1.0%
56992 1
1.0%
56993 1
1.0%
56994 1
1.0%
ValueCountFrequency (%)
57490 1
1.0%
57489 1
1.0%
57488 1
1.0%
57487 1
1.0%
57486 1
1.0%
57419 1
1.0%
57418 1
1.0%
57417 1
1.0%
57416 1
1.0%
57415 1
1.0%

격자번호
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:13:24.561636image/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 row라마7742
2nd row라마7743
3rd row라마7744
4th row라마7745
5th row라마7746
ValueCountFrequency (%)
라마7742 1
 
1.0%
라마8163 1
 
1.0%
라마8241 1
 
1.0%
라마8240 1
 
1.0%
라마8239 1
 
1.0%
라마8238 1
 
1.0%
라마8237 1
 
1.0%
라마8236 1
 
1.0%
라마8235 1
 
1.0%
라마8167 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T20:13:25.152325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
라 100
16.7%
마 100
16.7%
8 87
14.5%
4 49
8.2%
2 42
7.0%
3 38
 
6.3%
5 36
 
6.0%
7 35
 
5.8%
1 35
 
5.8%
6 33
 
5.5%
Other values (2) 45
7.5%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 87
21.8%
4 49
12.2%
2 42
10.5%
3 38
9.5%
5 36
9.0%
7 35
8.8%
1 35
8.8%
6 33
 
8.2%
0 26
 
6.5%
9 19
 
4.8%
Other Letter
ValueCountFrequency (%)
라 100
50.0%
마 100
50.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
8 87
21.8%
4 49
12.2%
2 42
10.5%
3 38
9.5%
5 36
9.0%
7 35
8.8%
1 35
8.8%
6 33
 
8.2%
0 26
 
6.5%
9 19
 
4.8%
Hangul
ValueCountFrequency (%)
라 100
50.0%
마 100
50.0%

Most occurring blocks

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

Most frequent character per block

Hangul
ValueCountFrequency (%)
라 100
50.0%
마 100
50.0%
ASCII
ValueCountFrequency (%)
8 87
21.8%
4 49
12.2%
2 42
10.5%
3 38
9.5%
5 36
9.0%
7 35
8.8%
1 35
8.8%
6 33
 
8.2%
0 26
 
6.5%
9 19
 
4.8%

시도코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
27
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row27
2nd row27
3rd row27
4th row27
5th row27

Common Values

ValueCountFrequency (%)
27 100
100.0%

Length

2023-12-10T20:13:25.329642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:25.456745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
27 100
100.0%

시도명
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구 100
100.0%

Length

2023-12-10T20:13:25.597918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:25.728270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구 100
100.0%

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
27710
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row27710
2nd row27710
3rd row27710
4th row27710
5th row27710

Common Values

ValueCountFrequency (%)
27710 100
100.0%

Length

2023-12-10T20:13:25.880463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:26.018323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
27710 100
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
달성군 100
100.0%

Length

2023-12-10T20:13:26.161036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:26.255612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
달성군 100
100.0%

기울기
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6901
Minimum5.42
Maximum20.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:13:26.337379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.42
5-th percentile5.42
Q17.83
median7.83
Q38.54
95-th percentile18.5885
Maximum20.65
Range15.23
Interquartile range (IQR)0.71

Descriptive statistics

Standard deviation3.4149882
Coefficient of variation (CV)0.39297456
Kurtosis6.6732232
Mean8.6901
Median Absolute Deviation (MAD)0.71
Skewness2.6737385
Sum869.01
Variance11.662144
MonotonicityNot monotonic
2023-12-10T20:13:26.774649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7.83 48
48.0%
8.54 18
 
18.0%
7.01 12
 
12.0%
5.42 10
 
10.0%
20.65 5
 
5.0%
11.48 4
 
4.0%
18.48 2
 
2.0%
15.0 1
 
1.0%
ValueCountFrequency (%)
5.42 10
 
10.0%
7.01 12
 
12.0%
7.83 48
48.0%
8.54 18
 
18.0%
11.48 4
 
4.0%
15.0 1
 
1.0%
18.48 2
 
2.0%
20.65 5
 
5.0%
ValueCountFrequency (%)
20.65 5
 
5.0%
18.48 2
 
2.0%
15.0 1
 
1.0%
11.48 4
 
4.0%
8.54 18
 
18.0%
7.83 48
48.0%
7.01 12
 
12.0%
5.42 10
 
10.0%

y절편
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-37.869
Minimum-93.76
Maximum-18.82
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)100.0%
Memory size1.0 KiB
2023-12-10T20:13:26.920299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-93.76
5-th percentile-82.873
Q1-38.22
median-35.27
Q3-35.27
95-th percentile-18.82
Maximum-18.82
Range74.94
Interquartile range (IQR)2.95

Descriptive statistics

Standard deviation16.405186
Coefficient of variation (CV)-0.43320886
Kurtosis5.6629981
Mean-37.869
Median Absolute Deviation (MAD)2.95
Skewness-2.3254327
Sum-3786.9
Variance269.13014
MonotonicityNot monotonic
2023-12-10T20:13:27.087648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
-35.27 48
48.0%
-38.22 18
 
18.0%
-26.34 12
 
12.0%
-18.82 10
 
10.0%
-93.76 5
 
5.0%
-49.92 4
 
4.0%
-82.3 2
 
2.0%
-68.62 1
 
1.0%
ValueCountFrequency (%)
-93.76 5
 
5.0%
-82.3 2
 
2.0%
-68.62 1
 
1.0%
-49.92 4
 
4.0%
-38.22 18
 
18.0%
-35.27 48
48.0%
-26.34 12
 
12.0%
-18.82 10
 
10.0%
ValueCountFrequency (%)
-18.82 10
 
10.0%
-26.34 12
 
12.0%
-35.27 48
48.0%
-38.22 18
 
18.0%
-49.92 4
 
4.0%
-68.62 1
 
1.0%
-82.3 2
 
2.0%
-93.76 5
 
5.0%

10cm 침수심 유발 강우량
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.0074
Minimum35.35
Maximum112.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:13:27.220939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.35
5-th percentile35.35
Q142.99
median42.99
Q347.18
95-th percentile103.012
Maximum112.74
Range77.39
Interquartile range (IQR)4.19

Descriptive statistics

Standard deviation18.002306
Coefficient of variation (CV)0.36733852
Kurtosis7.1906572
Mean49.0074
Median Absolute Deviation (MAD)0.75
Skewness2.8489573
Sum4900.74
Variance324.08302
MonotonicityNot monotonic
2023-12-10T20:13:27.377095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
42.99 48
48.0%
47.18 18
 
18.0%
43.74 12
 
12.0%
35.35 10
 
10.0%
112.74 5
 
5.0%
64.88 4
 
4.0%
102.5 2
 
2.0%
81.38 1
 
1.0%
ValueCountFrequency (%)
35.35 10
 
10.0%
42.99 48
48.0%
43.74 12
 
12.0%
47.18 18
 
18.0%
64.88 4
 
4.0%
81.38 1
 
1.0%
102.5 2
 
2.0%
112.74 5
 
5.0%
ValueCountFrequency (%)
112.74 5
 
5.0%
102.5 2
 
2.0%
81.38 1
 
1.0%
64.88 4
 
4.0%
47.18 18
 
18.0%
43.74 12
 
12.0%
42.99 48
48.0%
35.35 10
 
10.0%

20cm 침수심 유발 강우량
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.8916
Minimum89.52
Maximum319.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:13:27.516029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89.52
5-th percentile89.52
Q1121.26
median121.26
Q3132.59
95-th percentile288.897
Maximum319.24
Range229.72
Interquartile range (IQR)11.33

Descriptive statistics

Standard deviation52.081518
Coefficient of variation (CV)0.38325782
Kurtosis6.8995697
Mean135.8916
Median Absolute Deviation (MAD)7.43
Skewness2.7498246
Sum13589.16
Variance2712.4845
MonotonicityNot monotonic
2023-12-10T20:13:27.667542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
121.26 48
48.0%
132.59 18
 
18.0%
113.83 12
 
12.0%
89.52 10
 
10.0%
319.24 5
 
5.0%
179.68 4
 
4.0%
287.3 2
 
2.0%
231.38 1
 
1.0%
ValueCountFrequency (%)
89.52 10
 
10.0%
113.83 12
 
12.0%
121.26 48
48.0%
132.59 18
 
18.0%
179.68 4
 
4.0%
231.38 1
 
1.0%
287.3 2
 
2.0%
319.24 5
 
5.0%
ValueCountFrequency (%)
319.24 5
 
5.0%
287.3 2
 
2.0%
231.38 1
 
1.0%
179.68 4
 
4.0%
132.59 18
 
18.0%
121.26 48
48.0%
113.83 12
 
12.0%
89.52 10
 
10.0%

50cm 침수심 유발 강우량
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean396.5264
Minimum252.02
Maximum938.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:13:27.808180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum252.02
5-th percentile252.02
Q1356.05
median356.05
Q3388.8
95-th percentile846.552
Maximum938.74
Range686.72
Interquartile range (IQR)32.75

Descriptive statistics

Standard deviation154.53068
Coefficient of variation (CV)0.38971094
Kurtosis6.7533803
Mean396.5264
Median Absolute Deviation (MAD)31.98
Skewness2.7009034
Sum39652.64
Variance23879.73
MonotonicityNot monotonic
2023-12-10T20:13:27.957753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
356.05 48
48.0%
388.8 18
 
18.0%
324.07 12
 
12.0%
252.02 10
 
10.0%
938.74 5
 
5.0%
524.08 4
 
4.0%
841.7 2
 
2.0%
681.38 1
 
1.0%
ValueCountFrequency (%)
252.02 10
 
10.0%
324.07 12
 
12.0%
356.05 48
48.0%
388.8 18
 
18.0%
524.08 4
 
4.0%
681.38 1
 
1.0%
841.7 2
 
2.0%
938.74 5
 
5.0%
ValueCountFrequency (%)
938.74 5
 
5.0%
841.7 2
 
2.0%
681.38 1
 
1.0%
524.08 4
 
4.0%
388.8 18
 
18.0%
356.05 48
48.0%
324.07 12
 
12.0%
252.02 10
 
10.0%

Interactions

2023-12-10T20:13:22.864851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:19.515902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:20.145473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:20.841675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:21.492630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:22.200449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:22.952405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:19.610999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:20.237594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:20.938424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:21.604867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:22.323205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:23.058108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:19.723629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:20.328820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:21.052632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:21.719821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:22.439120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:23.189075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:19.850057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:20.437404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:21.167496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:21.844821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:22.555788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:23.330248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:19.950728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:20.544693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:21.272588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:21.965561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:22.680288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:23.434740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:20.052557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:20.670524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:21.384121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:22.070891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:13:22.762806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:13:28.093836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디격자번호기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
아이디1.0001.0000.1170.3510.1560.0000.000
격자번호1.0001.0001.0001.0001.0001.0001.000
기울기0.1171.0001.0000.9970.9691.0001.000
y절편0.3511.0000.9971.0000.9611.0001.000
10cm 침수심 유발 강우량0.1561.0000.9690.9611.0000.9951.000
20cm 침수심 유발 강우량0.0001.0001.0001.0000.9951.0001.000
50cm 침수심 유발 강우량0.0001.0001.0001.0001.0001.0001.000
2023-12-10T20:13:28.378105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
아이디1.000-0.0220.0220.151-0.022-0.022
기울기-0.0221.000-1.0000.7651.0001.000
y절편0.022-1.0001.000-0.765-1.000-1.000
10cm 침수심 유발 강우량0.1510.765-0.7651.0000.7650.765
20cm 침수심 유발 강우량-0.0221.000-1.0000.7651.0001.000
50cm 침수심 유발 강우량-0.0221.000-1.0000.7651.0001.000

Missing values

2023-12-10T20:13:23.600045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:13:23.774844image/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
056892라마774227대구27710달성군7.83-35.2742.99121.26356.05
156893라마774327대구27710달성군7.83-35.2742.99121.26356.05
256894라마774427대구27710달성군7.83-35.2742.99121.26356.05
356895라마774527대구27710달성군7.83-35.2742.99121.26356.05
456896라마774627대구27710달성군7.83-35.2742.99121.26356.05
556985라마783527대구27710달성군18.48-82.3102.5287.3841.7
656991라마784127대구27710달성군7.83-35.2742.99121.26356.05
756992라마784227대구27710달성군7.83-35.2742.99121.26356.05
856993라마784327대구27710달성군7.83-35.2742.99121.26356.05
956994라마784427대구27710달성군7.83-35.2742.99121.26356.05
아이디격자번호시도코드시도명시군구코드시군구명기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
9057415라마826527대구27710달성군5.42-18.8235.3589.52252.02
9157416라마826627대구27710달성군5.42-18.8235.3589.52252.02
9257417라마826727대구27710달성군7.01-26.3443.74113.83324.07
9357418라마826827대구27710달성군7.01-26.3443.74113.83324.07
9457419라마826927대구27710달성군7.01-26.3443.74113.83324.07
9557486라마833627대구27710달성군7.83-35.2742.99121.26356.05
9657487라마833727대구27710달성군7.83-35.2742.99121.26356.05
9757488라마833827대구27710달성군7.83-35.2742.99121.26356.05
9857489라마833927대구27710달성군7.83-35.2742.99121.26356.05
9957490라마834027대구27710달성군11.48-49.9264.88179.68524.08