Overview

Dataset statistics

Number of variables12
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.4 KiB
Average record size in memory106.3 B

Variable types

Numeric6
Text1
Categorical5

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
창고시설 is highly overall correlated with 공업High correlation
공업 is highly overall correlated with 창고시설High correlation
위험물저장처리시설 is highly imbalanced (70.1%)Imbalance
id has unique valuesUnique
gid has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:36:57.202078
Analysis finished2023-12-10 13:37:01.987664
Duration4.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:37:02.065884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T22:37:02.204592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

gid
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:37:02.511862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600
Distinct characters11
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 row나나7577
2nd row나나7578
3rd row나나7580
4th row나나7582
5th row나나7583
ValueCountFrequency (%)
나나7577 1
 
1.0%
나나8078 1
 
1.0%
나나8172 1
 
1.0%
나나8088 1
 
1.0%
나나8087 1
 
1.0%
나나8086 1
 
1.0%
나나8085 1
 
1.0%
나나8084 1
 
1.0%
나나8083 1
 
1.0%
나나8082 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T22:37:02.957794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 200
33.3%
7 130
21.7%
8 117
19.5%
1 27
 
4.5%
0 24
 
4.0%
9 23
 
3.8%
6 21
 
3.5%
2 18
 
3.0%
5 16
 
2.7%
3 12
 
2.0%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 130
32.5%
8 117
29.2%
1 27
 
6.8%
0 24
 
6.0%
9 23
 
5.8%
6 21
 
5.2%
2 18
 
4.5%
5 16
 
4.0%
3 12
 
3.0%
4 12
 
3.0%
Other Letter
ValueCountFrequency (%)
나 200
100.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
7 130
32.5%
8 117
29.2%
1 27
 
6.8%
0 24
 
6.0%
9 23
 
5.8%
6 21
 
5.2%
2 18
 
4.5%
5 16
 
4.0%
3 12
 
3.0%
4 12
 
3.0%
Hangul
ValueCountFrequency (%)
나 200
100.0%

Most occurring blocks

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

Most frequent character per block

Hangul
ValueCountFrequency (%)
나 200
100.0%
ASCII
ValueCountFrequency (%)
7 130
32.5%
8 117
29.2%
1 27
 
6.8%
0 24
 
6.0%
9 23
 
5.8%
6 21
 
5.2%
2 18
 
4.5%
5 16
 
4.0%
3 12
 
3.0%
4 12
 
3.0%

SD_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
50
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50
2nd row50
3rd row50
4th row50
5th row50

Common Values

ValueCountFrequency (%)
50 100
100.0%

Length

2023-12-10T22:37:03.186750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:37:03.310445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50 100
100.0%

SD_NM
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주 100
100.0%

Length

2023-12-10T22:37:03.426425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:37:03.532898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주 100
100.0%

SGG_CD
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
50110
69 
50130
31 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50110
2nd row50110
3rd row50110
4th row50110
5th row50110

Common Values

ValueCountFrequency (%)
50110 69
69.0%
50130 31
31.0%

Length

2023-12-10T22:37:03.645819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:37:03.742753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50110 69
69.0%
50130 31
31.0%

SGG_KOR_NM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
제주시
69 
서귀포시
31 

Length

Max length4
Median length3
Mean length3.31
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 69
69.0%
서귀포시 31
31.0%

Length

2023-12-10T22:37:03.861162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:37:04.010911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 69
69.0%
서귀포시 31
31.0%

공장
Real number (ℝ)

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-75.53
Minimum-99
Maximum27
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)77.0%
Memory size1.0 KiB
2023-12-10T22:37:04.129058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q1-99
median-99
Q3-99
95-th percentile2.05
Maximum27
Range126
Interquartile range (IQR)0

Descriptive statistics

Standard deviation43.240566
Coefficient of variation (CV)-0.57249525
Kurtosis-0.23693682
Mean-75.53
Median Absolute Deviation (MAD)0
Skewness1.3180764
Sum-7553
Variance1869.7466
MonotonicityNot monotonic
2023-12-10T22:37:04.255533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-99 77
77.0%
1 16
 
16.0%
2 2
 
2.0%
8 2
 
2.0%
27 1
 
1.0%
3 1
 
1.0%
4 1
 
1.0%
ValueCountFrequency (%)
-99 77
77.0%
1 16
 
16.0%
2 2
 
2.0%
3 1
 
1.0%
4 1
 
1.0%
8 2
 
2.0%
27 1
 
1.0%
ValueCountFrequency (%)
27 1
 
1.0%
8 2
 
2.0%
4 1
 
1.0%
3 1
 
1.0%
2 2
 
2.0%
1 16
 
16.0%
-99 77
77.0%

동식물관련시설
Real number (ℝ)

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-30.61
Minimum-99
Maximum69
Zeros0
Zeros (%)0.0%
Negative39
Negative (%)39.0%
Memory size1.0 KiB
2023-12-10T22:37:04.391721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q1-99
median3
Q38.25
95-th percentile33.2
Maximum69
Range168
Interquartile range (IQR)107.25

Descriptive statistics

Standard deviation56.215655
Coefficient of variation (CV)-1.8365128
Kurtosis-1.6692306
Mean-30.61
Median Absolute Deviation (MAD)22
Skewness-0.29940503
Sum-3061
Variance3160.1999
MonotonicityNot monotonic
2023-12-10T22:37:04.546596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
-99 39
39.0%
3 7
 
7.0%
4 7
 
7.0%
2 5
 
5.0%
7 5
 
5.0%
6 4
 
4.0%
1 4
 
4.0%
5 3
 
3.0%
16 2
 
2.0%
9 2
 
2.0%
Other values (19) 22
22.0%
ValueCountFrequency (%)
-99 39
39.0%
1 4
 
4.0%
2 5
 
5.0%
3 7
 
7.0%
4 7
 
7.0%
5 3
 
3.0%
6 4
 
4.0%
7 5
 
5.0%
8 1
 
1.0%
9 2
 
2.0%
ValueCountFrequency (%)
69 1
1.0%
58 1
1.0%
55 1
1.0%
51 1
1.0%
37 1
1.0%
33 1
1.0%
30 1
1.0%
29 1
1.0%
27 1
1.0%
26 2
2.0%

위험물저장처리시설
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-99
90 
1
 
6
2
 
3
3
 
1

Length

Max length3
Median length3
Mean length2.8
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row-99
2nd row-99
3rd row-99
4th row-99
5th row-99

Common Values

ValueCountFrequency (%)
-99 90
90.0%
1 6
 
6.0%
2 3
 
3.0%
3 1
 
1.0%

Length

2023-12-10T22:37:04.688200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:37:04.826994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
99 90
90.0%
1 6
 
6.0%
2 3
 
3.0%
3 1
 
1.0%

자동차관련시설
Real number (ℝ)

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-79.84
Minimum-99
Maximum6
Zeros0
Zeros (%)0.0%
Negative81
Negative (%)81.0%
Memory size1.0 KiB
2023-12-10T22:37:04.937953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q1-99
median-99
Q3-99
95-th percentile2
Maximum6
Range105
Interquartile range (IQR)0

Descriptive statistics

Standard deviation39.764641
Coefficient of variation (CV)-0.49805412
Kurtosis0.5909761
Mean-79.84
Median Absolute Deviation (MAD)0
Skewness1.6055609
Sum-7984
Variance1581.2267
MonotonicityNot monotonic
2023-12-10T22:37:05.092107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
-99 81
81.0%
1 12
 
12.0%
2 3
 
3.0%
3 2
 
2.0%
5 1
 
1.0%
6 1
 
1.0%
ValueCountFrequency (%)
-99 81
81.0%
1 12
 
12.0%
2 3
 
3.0%
3 2
 
2.0%
5 1
 
1.0%
6 1
 
1.0%
ValueCountFrequency (%)
6 1
 
1.0%
5 1
 
1.0%
3 2
 
2.0%
2 3
 
3.0%
1 12
 
12.0%
-99 81
81.0%

창고시설
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.6
Minimum1
Maximum269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:37:05.266139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median35.5
Q391.25
95-th percentile215.1
Maximum269
Range268
Interquartile range (IQR)83.25

Descriptive statistics

Standard deviation67.942786
Coefficient of variation (CV)1.1399796
Kurtosis1.7342746
Mean59.6
Median Absolute Deviation (MAD)32
Skewness1.5000544
Sum5960
Variance4616.2222
MonotonicityNot monotonic
2023-12-10T22:37:05.829212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 6
 
6.0%
8 6
 
6.0%
1 4
 
4.0%
71 4
 
4.0%
2 4
 
4.0%
6 3
 
3.0%
9 3
 
3.0%
10 3
 
3.0%
69 2
 
2.0%
12 2
 
2.0%
Other values (57) 63
63.0%
ValueCountFrequency (%)
1 4
4.0%
2 4
4.0%
3 6
6.0%
4 2
 
2.0%
5 2
 
2.0%
6 3
3.0%
7 2
 
2.0%
8 6
6.0%
9 3
3.0%
10 3
3.0%
ValueCountFrequency (%)
269 1
1.0%
268 1
1.0%
264 1
1.0%
238 1
1.0%
236 1
1.0%
214 1
1.0%
200 1
1.0%
174 1
1.0%
168 1
1.0%
166 1
1.0%

공업
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.8
Minimum1
Maximum280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:37:06.050832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114.75
median44.5
Q397.25
95-th percentile221.75
Maximum280
Range279
Interquartile range (IQR)82.5

Descriptive statistics

Standard deviation69.212453
Coefficient of variation (CV)1.005995
Kurtosis1.3092356
Mean68.8
Median Absolute Deviation (MAD)35.5
Skewness1.373151
Sum6880
Variance4790.3636
MonotonicityNot monotonic
2023-12-10T22:37:06.240451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 5
 
5.0%
27 3
 
3.0%
3 3
 
3.0%
49 3
 
3.0%
1 3
 
3.0%
13 3
 
3.0%
107 2
 
2.0%
9 2
 
2.0%
25 2
 
2.0%
14 2
 
2.0%
Other values (65) 72
72.0%
ValueCountFrequency (%)
1 3
3.0%
2 1
 
1.0%
3 3
3.0%
5 1
 
1.0%
6 1
 
1.0%
7 1
 
1.0%
8 5
5.0%
9 2
 
2.0%
10 2
 
2.0%
12 1
 
1.0%
ValueCountFrequency (%)
280 1
1.0%
276 1
1.0%
265 1
1.0%
247 1
1.0%
236 1
1.0%
221 1
1.0%
210 1
1.0%
197 1
1.0%
179 1
1.0%
175 1
1.0%

Interactions

2023-12-10T22:37:01.110748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:57.667870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:58.623455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:59.279667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:59.945034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:00.532275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:01.202394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:57.771020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:58.724405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:59.430087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:00.033862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:00.640040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:01.289974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:57.886611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:58.817592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:59.536553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:00.125441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:00.733522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:01.380555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:57.996511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:58.918819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:59.644697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:00.221191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:00.830615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:01.467086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:58.397185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:59.013082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:59.750280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:00.314231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:00.920327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:01.565233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:58.495695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:59.110784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:59.847872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:00.411954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:37:01.013782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:37:06.375167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idgidSGG_CDSGG_KOR_NM공장동식물관련시설위험물저장처리시설자동차관련시설창고시설공업
id1.0001.0000.2900.2900.1560.0000.0000.0000.2070.000
gid1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
SGG_CD0.2901.0001.0000.9990.3190.3820.0000.3600.0530.381
SGG_KOR_NM0.2901.0000.9991.0000.3190.3820.0000.3600.0530.381
공장0.1561.0000.3190.3191.0000.4380.4350.1810.0000.292
동식물관련시설0.0001.0000.3820.3820.4381.0000.0000.0000.0000.162
위험물저장처리시설0.0001.0000.0000.0000.4350.0001.0000.2000.4840.508
자동차관련시설0.0001.0000.3600.3600.1810.0000.2001.0000.6490.549
창고시설0.2071.0000.0530.0530.0000.0000.4840.6491.0000.988
공업0.0001.0000.3810.3810.2920.1620.5080.5490.9881.000
2023-12-10T22:37:06.566513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위험물저장처리시설SGG_CDSGG_KOR_NM
위험물저장처리시설1.0000.0000.000
SGG_CD0.0001.0000.976
SGG_KOR_NM0.0000.9761.000
2023-12-10T22:37:06.702997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
id공장동식물관련시설자동차관련시설창고시설공업SGG_CDSGG_KOR_NM위험물저장처리시설
id1.0000.058-0.118-0.0470.015-0.0500.2110.2110.000
공장0.0581.0000.1670.1680.0910.1890.2100.2100.180
동식물관련시설-0.1180.1671.000-0.066-0.0270.2320.2680.2680.000
자동차관련시설-0.0470.168-0.0661.0000.4420.4540.2350.2350.130
창고시설0.0150.091-0.0270.4421.0000.9300.0240.0240.299
공업-0.0500.1890.2320.4540.9301.0000.2790.2790.317
SGG_CD0.2110.2100.2680.2350.0240.2791.0000.9760.000
SGG_KOR_NM0.2110.2100.2680.2350.0240.2790.9761.0000.000
위험물저장처리시설0.0000.1800.0000.1300.2990.3170.0000.0001.000

Missing values

2023-12-10T22:37:01.697113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:37:01.907992image/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_NM공장동식물관련시설위험물저장처리시설자동차관련시설창고시설공업
01나나757750제주50110제주시-9927-99-99330
12나나757850제주50110제주시-994-99-99103107
23나나758050제주50110제주시-99-99-99-991616
34나나758250제주50110제주시-993-99-997174
45나나758350제주50110제주시-9911-99-99617
56나나767650제주50130서귀포시-99-99-99-994949
67나나767750제주50110제주시-992-99-996567
78나나767850제주50110제주시-9919-99-99524
89나나767950제주50110제주시17-99-993644
910나나768050제주50110제주시-99-99-9918687
idgidSD_CDSD_NMSGG_CDSGG_KOR_NM공장동식물관련시설위험물저장처리시설자동차관련시설창고시설공업
9091나나818950제주50110제주시-99-991-99264265
9192나나819150제주50110제주시-99-99-99-997676
9293나나827150제주50130서귀포시-99-99-991118119
9394나나827250제주50130서귀포시-9919-99-996382
9495나나827350제주50130서귀포시115-99-99925
9596나나827450제주50130서귀포시-9955-99-99142197
9697나나827550제주50130서귀포시124-99-996994
9798나나827650제주50110제주시1-99-99-994748
9899나나827750제주50110제주시-99-99-99-9988
99100나나827850제주50110제주시-994-99-99104108