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

SD_CD has constant value ""Constant
SD_NM has constant value ""Constant
SGG_CD is highly overall correlated with SGG_KOR_NMHigh correlation
SGG_KOR_NM is highly overall correlated with SGG_CDHigh correlation
id is highly overall correlated with interceptHigh correlation
inclination is highly overall correlated with intercept and 2 other fieldsHigh correlation
intercept is highly overall correlated with id and 3 other fieldsHigh correlation
Depth_20 is highly overall correlated with inclination and 2 other fieldsHigh correlation
Depth_50 is highly overall correlated with inclination and 2 other fieldsHigh correlation
id has unique valuesUnique
gid has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:49:43.015756
Analysis finished2023-12-10 10:49:49.033541
Duration6.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
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
2023-12-10T19:49:49.569854image/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
2023-12-10T19:49:49.852297image/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%

gid
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:49:50.381444image/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%
2023-12-10T19:49:51.115274image/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%

SD_CD
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

2023-12-10T19:49:51.370883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:49:51.558982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 100
100.0%

SD_NM
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

2023-12-10T19:49:51.715248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:49:51.867199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 100
100.0%

SGG_CD
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

2023-12-10T19:49:52.020177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:49:52.177949image/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%

SGG_KOR_NM
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

2023-12-10T19:49:52.378444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:49:52.553930image/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%

inclination
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
2023-12-10T19:49:52.740238image/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
2023-12-10T19:49:52.907441image/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%

intercept
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
2023-12-10T19:49:53.075804image/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
2023-12-10T19:49:53.294548image/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%

Depth_10
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
2023-12-10T19:49:53.503542image/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
2023-12-10T19:49:53.697758image/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%

Depth_20
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
2023-12-10T19:49:53.863791image/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
2023-12-10T19:49:54.035495image/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%

Depth_50
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
2023-12-10T19:49:54.180719image/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
2023-12-10T19:49:54.344478image/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

2023-12-10T19:49:47.798141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:43.553337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:44.410387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:45.237697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:46.056073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:46.958586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:47.949369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:43.690214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:44.568230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:45.375420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:46.192704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:47.101385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:48.077522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:43.820589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:44.695102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:45.511353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:46.325849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:47.217139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:48.265328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:44.015375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:44.854739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:45.657016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:46.476200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:47.371788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:48.412595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:44.144359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:45.027627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:45.773727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:46.716408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:47.507051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:48.549818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:44.262583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:45.114295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:45.904211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:46.831361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:47.639847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:49:54.605198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idgidSGG_CDSGG_KOR_NMinclinationinterceptDepth_10Depth_20Depth_50
id1.0001.0000.5690.5690.3460.0350.4070.6260.450
gid1.0001.0001.0001.0001.0001.0001.0001.0001.000
SGG_CD0.5691.0001.0001.0000.3810.2590.5580.6040.633
SGG_KOR_NM0.5691.0001.0001.0000.3810.2590.5580.6040.633
inclination0.3461.0000.3810.3811.0001.0000.9981.0001.000
intercept0.0351.0000.2590.2591.0001.0000.8121.0001.000
Depth_100.4071.0000.5580.5580.9980.8121.0001.0000.996
Depth_200.6261.0000.6040.6041.0001.0001.0001.0001.000
Depth_500.4501.0000.6330.6331.0001.0000.9961.0001.000
2023-12-10T19:49:54.810932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SGG_CDSGG_KOR_NM
SGG_CD1.0001.000
SGG_KOR_NM1.0001.000
2023-12-10T19:49:54.961545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idinclinationinterceptDepth_10Depth_20Depth_50SGG_CDSGG_KOR_NM
id1.0000.402-0.637-0.4080.4020.4020.2660.266
inclination0.4021.000-0.7490.1761.0001.0000.2670.267
intercept-0.637-0.7491.0000.455-0.749-0.7490.1230.123
Depth_10-0.4080.1760.4551.0000.1760.1760.4170.417
Depth_200.4021.000-0.7490.1761.0001.0000.4400.440
Depth_500.4021.000-0.7490.1761.0001.0000.2670.267
SGG_CD0.2660.2670.1230.4170.4400.2671.0001.000
SGG_KOR_NM0.2660.2670.1230.4170.4400.2671.0001.000

Missing values

2023-12-10T19:49:48.709551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:49:48.947954image/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

idgidSD_CDSD_NMSGG_CDSGG_KOR_NMinclinationinterceptDepth_10Depth_20Depth_50
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
idgidSD_CDSD_NMSGG_CDSGG_KOR_NMinclinationinterceptDepth_10Depth_20Depth_50
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