Overview

Dataset statistics

Number of variables12
Number of observations27
Missing cells49
Missing cells (%)15.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory111.9 B

Variable types

Text1
Numeric8
Categorical3

Alerts

메탈(0~99W)(개소) is highly overall correlated with 메탈(100~150W)(개소) and 4 other fieldsHigh correlation
메탈(301~400W)(개소) is highly overall correlated with LED(0~49W)(개소) and 1 other fieldsHigh correlation
나트륨(100~200W)(개소) is highly overall correlated with LED(0~49W)(개소)High correlation
나트륨(201~300W)(개소) is highly overall correlated with 나트륨(301~400W)(개소)High correlation
메탈(100~150W)(개소) is highly overall correlated with LED(0~49W)(개소) and 1 other fieldsHigh correlation
메탈(151~300W)(개소) is highly overall correlated with LED(0~49W)(개소) and 3 other fieldsHigh correlation
LED(0~49W)(개소) is highly overall correlated with 나트륨(100~200W)(개소) and 7 other fieldsHigh correlation
LED(50~100W)(개소) is highly overall correlated with 나트륨(301~400W)(개소)High correlation
LED(101~120W)(개소) is highly overall correlated with 메탈(151~300W)(개소) and 3 other fieldsHigh correlation
LED(121~150W)(개소) is highly overall correlated with 메탈(151~300W)(개소) and 2 other fieldsHigh correlation
나트륨(301~400W)(개소) is highly overall correlated with 나트륨(201~300W)(개소) and 2 other fieldsHigh correlation
나트륨(100~200W)(개소) has 4 (14.8%) missing valuesMissing
나트륨(201~300W)(개소) has 6 (22.2%) missing valuesMissing
메탈(100~150W)(개소) has 5 (18.5%) missing valuesMissing
메탈(151~300W)(개소) has 5 (18.5%) missing valuesMissing
LED(0~49W)(개소) has 18 (66.7%) missing valuesMissing
LED(101~120W)(개소) has 5 (18.5%) missing valuesMissing
LED(121~150W)(개소) has 6 (22.2%) missing valuesMissing
시군명 has unique valuesUnique
LED(50~100W)(개소) has unique valuesUnique
나트륨(100~200W)(개소) has 2 (7.4%) zerosZeros
나트륨(201~300W)(개소) has 13 (48.1%) zerosZeros
메탈(100~150W)(개소) has 4 (14.8%) zerosZeros
메탈(151~300W)(개소) has 16 (59.3%) zerosZeros
LED(0~49W)(개소) has 4 (14.8%) zerosZeros
LED(101~120W)(개소) has 14 (51.9%) zerosZeros
LED(121~150W)(개소) has 15 (55.6%) zerosZeros

Reproduction

Analysis started2024-03-23 01:50:47.562610
Analysis finished2024-03-23 01:51:09.542272
Duration21.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-03-23T01:51:09.801818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0740741
Min length3

Characters and Unicode

Total characters83
Distinct characters33
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

Unique27 ?
Unique (%)100.0%

Sample

1st row가평군
2nd row고양시
3rd row과천시
4th row광명시
5th row광주시
ValueCountFrequency (%)
가평군 1
 
3.7%
안성시 1
 
3.7%
포천시 1
 
3.7%
평택시 1
 
3.7%
이천시 1
 
3.7%
의정부시 1
 
3.7%
의왕시 1
 
3.7%
용인시 1
 
3.7%
연천군 1
 
3.7%
여주시 1
 
3.7%
Other values (17) 17
63.0%
2024-03-23T01:51:10.743005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 25
30.1%
양 5
 
6.0%
천 5
 
6.0%
군 4
 
4.8%
주 4
 
4.8%
안 3
 
3.6%
평 3
 
3.6%
남 3
 
3.6%
포 3
 
3.6%
의 2
 
2.4%
Other values (23) 26
31.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 25
30.1%
양 5
 
6.0%
천 5
 
6.0%
군 4
 
4.8%
주 4
 
4.8%
안 3
 
3.6%
평 3
 
3.6%
남 3
 
3.6%
포 3
 
3.6%
의 2
 
2.4%
Other values (23) 26
31.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 25
30.1%
양 5
 
6.0%
천 5
 
6.0%
군 4
 
4.8%
주 4
 
4.8%
안 3
 
3.6%
평 3
 
3.6%
남 3
 
3.6%
포 3
 
3.6%
의 2
 
2.4%
Other values (23) 26
31.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 25
30.1%
양 5
 
6.0%
천 5
 
6.0%
군 4
 
4.8%
주 4
 
4.8%
안 3
 
3.6%
평 3
 
3.6%
남 3
 
3.6%
포 3
 
3.6%
의 2
 
2.4%
Other values (23) 26
31.3%

나트륨(100~200W)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct22
Distinct (%)95.7%
Missing4
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean2510.7391
Minimum0
Maximum12861
Zeros2
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-23T01:51:11.170701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q1161
median1020
Q33271
95-th percentile11240.8
Maximum12861
Range12861
Interquartile range (IQR)3110

Descriptive statistics

Standard deviation3588.6493
Coefficient of variation (CV)1.4293199
Kurtosis3.5818683
Mean2510.7391
Median Absolute Deviation (MAD)994
Skewness2.0436974
Sum57747
Variance12878404
MonotonicityNot monotonic
2024-03-23T01:51:11.612124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 2
 
7.4%
975 1
 
3.7%
1700 1
 
3.7%
26 1
 
3.7%
1183 1
 
3.7%
796 1
 
3.7%
1020 1
 
3.7%
2637 1
 
3.7%
101 1
 
3.7%
177 1
 
3.7%
Other values (12) 12
44.4%
(Missing) 4
 
14.8%
ValueCountFrequency (%)
0 2
7.4%
6 1
3.7%
26 1
3.7%
101 1
3.7%
145 1
3.7%
177 1
3.7%
178 1
3.7%
796 1
3.7%
975 1
3.7%
979 1
3.7%
ValueCountFrequency (%)
12861 1
3.7%
11595 1
3.7%
8053 1
3.7%
3519 1
3.7%
3365 1
3.7%
3364 1
3.7%
3178 1
3.7%
2637 1
3.7%
1889 1
3.7%
1700 1
3.7%

나트륨(201~300W)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)42.9%
Missing6
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean885.85714
Minimum0
Maximum9536
Zeros13
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-23T01:51:11.999894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q362
95-th percentile3406
Maximum9536
Range9536
Interquartile range (IQR)62

Descriptive statistics

Standard deviation2252.336
Coefficient of variation (CV)2.5425499
Kurtosis11.39721
Mean885.85714
Median Absolute Deviation (MAD)0
Skewness3.2229573
Sum18603
Variance5073017.4
MonotonicityNot monotonic
2024-03-23T01:51:12.413784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 13
48.1%
1 1
 
3.7%
9536 1
 
3.7%
25 1
 
3.7%
3135 1
 
3.7%
62 1
 
3.7%
79 1
 
3.7%
3406 1
 
3.7%
2359 1
 
3.7%
(Missing) 6
22.2%
ValueCountFrequency (%)
0 13
48.1%
1 1
 
3.7%
25 1
 
3.7%
62 1
 
3.7%
79 1
 
3.7%
2359 1
 
3.7%
3135 1
 
3.7%
3406 1
 
3.7%
9536 1
 
3.7%
ValueCountFrequency (%)
9536 1
 
3.7%
3406 1
 
3.7%
3135 1
 
3.7%
2359 1
 
3.7%
79 1
 
3.7%
62 1
 
3.7%
25 1
 
3.7%
1 1
 
3.7%
0 13
48.1%

나트륨(301~400W)(개소)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
0
17 
<NA>
8 
1
2 

Length

Max length4
Median length1
Mean length1.8888889
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 17
63.0%
<NA> 8
29.6%
1 2
 
7.4%

Length

2024-03-23T01:51:12.852054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T01:51:13.250266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17
63.0%
na 8
29.6%
1 2
 
7.4%

메탈(0~99W)(개소)
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size348.0 B
<NA>
20 
0
3 
6306
 
1
50
 
1
3955
 
1

Length

Max length4
Median length4
Mean length3.5925926
Min length1

Unique

Unique4 ?
Unique (%)14.8%

Sample

1st row6306
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 20
74.1%
0 3
 
11.1%
6306 1
 
3.7%
50 1
 
3.7%
3955 1
 
3.7%
3024 1
 
3.7%

Length

2024-03-23T01:51:13.661818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T01:51:14.219751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 20
74.1%
0 3
 
11.1%
6306 1
 
3.7%
50 1
 
3.7%
3955 1
 
3.7%
3024 1
 
3.7%

메탈(100~150W)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)86.4%
Missing5
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2586.8636
Minimum0
Maximum12892
Zeros4
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-23T01:51:14.520190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median932.5
Q32529.25
95-th percentile11664.05
Maximum12892
Range12892
Interquartile range (IQR)2520.25

Descriptive statistics

Standard deviation4060.5643
Coefficient of variation (CV)1.5696863
Kurtosis2.0683269
Mean2586.8636
Median Absolute Deviation (MAD)932.5
Skewness1.8130886
Sum56911
Variance16488183
MonotonicityNot monotonic
2024-03-23T01:51:14.915005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 4
14.8%
1973 1
 
3.7%
2653 1
 
3.7%
1 1
 
3.7%
345 1
 
3.7%
1056 1
 
3.7%
11698 1
 
3.7%
30 1
 
3.7%
2 1
 
3.7%
11019 1
 
3.7%
Other values (9) 9
33.3%
(Missing) 5
18.5%
ValueCountFrequency (%)
0 4
14.8%
1 1
 
3.7%
2 1
 
3.7%
30 1
 
3.7%
124 1
 
3.7%
218 1
 
3.7%
345 1
 
3.7%
911 1
 
3.7%
954 1
 
3.7%
1056 1
 
3.7%
ValueCountFrequency (%)
12892 1
3.7%
11698 1
3.7%
11019 1
3.7%
6043 1
3.7%
3461 1
3.7%
2653 1
3.7%
2158 1
3.7%
1973 1
3.7%
1373 1
3.7%
1056 1
3.7%

메탈(151~300W)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)31.8%
Missing5
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean237.72727
Minimum0
Maximum4758
Zeros16
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-23T01:51:15.266033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile400.15
Maximum4758
Range4758
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1013.5421
Coefficient of variation (CV)4.2634659
Kurtosis21.608612
Mean237.72727
Median Absolute Deviation (MAD)0
Skewness4.633867
Sum5230
Variance1027267.6
MonotonicityNot monotonic
2024-03-23T01:51:15.693423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 16
59.3%
4758 1
 
3.7%
23 1
 
3.7%
420 1
 
3.7%
5 1
 
3.7%
20 1
 
3.7%
4 1
 
3.7%
(Missing) 5
 
18.5%
ValueCountFrequency (%)
0 16
59.3%
4 1
 
3.7%
5 1
 
3.7%
20 1
 
3.7%
23 1
 
3.7%
420 1
 
3.7%
4758 1
 
3.7%
ValueCountFrequency (%)
4758 1
 
3.7%
420 1
 
3.7%
23 1
 
3.7%
20 1
 
3.7%
5 1
 
3.7%
4 1
 
3.7%
0 16
59.3%

메탈(301~400W)(개소)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
0
19 
<NA>
7 
1123
 
1

Length

Max length4
Median length1
Mean length1.8888889
Min length1

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st row<NA>
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
70.4%
<NA> 7
 
25.9%
1123 1
 
3.7%

Length

2024-03-23T01:51:16.083754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T01:51:16.380501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19
70.4%
na 7
 
25.9%
1123 1
 
3.7%

LED(0~49W)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)66.7%
Missing18
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean2143.2222
Minimum0
Maximum14603
Zeros4
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-23T01:51:16.683968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q31041
95-th percentile9949.4
Maximum14603
Range14603
Interquartile range (IQR)1041

Descriptive statistics

Standard deviation4772.9615
Coefficient of variation (CV)2.2270026
Kurtosis7.9254849
Mean2143.2222
Median Absolute Deviation (MAD)10
Skewness2.7805851
Sum19289
Variance22781162
MonotonicityNot monotonic
2024-03-23T01:51:17.031949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 4
 
14.8%
2969 1
 
3.7%
1041 1
 
3.7%
10 1
 
3.7%
14603 1
 
3.7%
666 1
 
3.7%
(Missing) 18
66.7%
ValueCountFrequency (%)
0 4
14.8%
10 1
 
3.7%
666 1
 
3.7%
1041 1
 
3.7%
2969 1
 
3.7%
14603 1
 
3.7%
ValueCountFrequency (%)
14603 1
 
3.7%
2969 1
 
3.7%
1041 1
 
3.7%
666 1
 
3.7%
10 1
 
3.7%
0 4
14.8%

LED(50~100W)(개소)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2857.5556
Minimum20
Maximum17543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-23T01:51:17.560771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile70.2
Q1185.5
median464
Q33102.5
95-th percentile13268.6
Maximum17543
Range17523
Interquartile range (IQR)2917

Descriptive statistics

Standard deviation4644.0219
Coefficient of variation (CV)1.6251729
Kurtosis3.8183776
Mean2857.5556
Median Absolute Deviation (MAD)384
Skewness2.0890704
Sum77154
Variance21566939
MonotonicityNot monotonic
2024-03-23T01:51:18.048166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1944 1
 
3.7%
66 1
 
3.7%
20 1
 
3.7%
7594 1
 
3.7%
85 1
 
3.7%
14279 1
 
3.7%
3819 1
 
3.7%
80 1
 
3.7%
433 1
 
3.7%
2386 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
20 1
3.7%
66 1
3.7%
80 1
3.7%
81 1
3.7%
85 1
3.7%
136 1
3.7%
182 1
3.7%
189 1
3.7%
239 1
3.7%
284 1
3.7%
ValueCountFrequency (%)
17543 1
3.7%
14279 1
3.7%
10911 1
3.7%
7594 1
3.7%
5732 1
3.7%
5300 1
3.7%
3819 1
3.7%
2386 1
3.7%
2250 1
3.7%
1944 1
3.7%

LED(101~120W)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)40.9%
Missing5
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean239.13636
Minimum0
Maximum4268
Zeros14
Zeros (%)51.9%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-23T01:51:18.448588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile742.4
Maximum4268
Range4268
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation914.92015
Coefficient of variation (CV)3.8259349
Kurtosis20.373259
Mean239.13636
Median Absolute Deviation (MAD)0
Skewness4.4661732
Sum5261
Variance837078.89
MonotonicityNot monotonic
2024-03-23T01:51:18.800827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 14
51.9%
33 1
 
3.7%
142 1
 
3.7%
14 1
 
3.7%
18 1
 
3.7%
774 1
 
3.7%
4 1
 
3.7%
4268 1
 
3.7%
8 1
 
3.7%
(Missing) 5
 
18.5%
ValueCountFrequency (%)
0 14
51.9%
4 1
 
3.7%
8 1
 
3.7%
14 1
 
3.7%
18 1
 
3.7%
33 1
 
3.7%
142 1
 
3.7%
774 1
 
3.7%
4268 1
 
3.7%
ValueCountFrequency (%)
4268 1
 
3.7%
774 1
 
3.7%
142 1
 
3.7%
33 1
 
3.7%
18 1
 
3.7%
14 1
 
3.7%
8 1
 
3.7%
4 1
 
3.7%
0 14
51.9%

LED(121~150W)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)33.3%
Missing6
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean63.095238
Minimum0
Maximum1001
Zeros15
Zeros (%)55.6%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-23T01:51:19.222828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile169
Maximum1001
Range1001
Interquartile range (IQR)12

Descriptive statistics

Standard deviation218.72149
Coefficient of variation (CV)3.4665293
Kurtosis19.3402
Mean63.095238
Median Absolute Deviation (MAD)0
Skewness4.342539
Sum1325
Variance47839.09
MonotonicityNot monotonic
2024-03-23T01:51:19.750578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 15
55.6%
12 1
 
3.7%
88 1
 
3.7%
13 1
 
3.7%
42 1
 
3.7%
169 1
 
3.7%
1001 1
 
3.7%
(Missing) 6
 
22.2%
ValueCountFrequency (%)
0 15
55.6%
12 1
 
3.7%
13 1
 
3.7%
42 1
 
3.7%
88 1
 
3.7%
169 1
 
3.7%
1001 1
 
3.7%
ValueCountFrequency (%)
1001 1
 
3.7%
169 1
 
3.7%
88 1
 
3.7%
42 1
 
3.7%
13 1
 
3.7%
12 1
 
3.7%
0 15
55.6%

Interactions

2024-03-23T01:51:05.637275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:48.308891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:50.666850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:52.894770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:55.305205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:57.950103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:00.318249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:02.823310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:06.000812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:48.610675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:51.006206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:53.134316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:55.583436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:58.222660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:00.620676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:03.096423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:06.317741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:48.855391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:51.252738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:53.457844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:55.936991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:58.524001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:00.961897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:03.523933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:06.575036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:49.157421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:51.533347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:53.744481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:56.424834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:58.785292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:01.210351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:04.001822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:06.791255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:49.419440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:51.784705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:54.017802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:56.695881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:59.054794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:01.441261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:04.286448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:07.061138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:49.721366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:52.112558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:54.287703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:57.025970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:59.392433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:01.801325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:04.719550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:07.323349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:50.044322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:52.409657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:54.611602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:57.404310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:59.711025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:02.273845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:05.002441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:07.594981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:50.336566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:52.678995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:54.996704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:50:57.708180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:00.015826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:02.584975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:51:05.359566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T01:51:20.035824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명나트륨(100~200W)(개소)나트륨(201~300W)(개소)나트륨(301~400W)(개소)메탈(0~99W)(개소)메탈(100~150W)(개소)메탈(151~300W)(개소)메탈(301~400W)(개소)LED(0~49W)(개소)LED(50~100W)(개소)LED(101~120W)(개소)LED(121~150W)(개소)
시군명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
나트륨(100~200W)(개소)1.0001.0000.4440.0000.0000.6180.0000.0000.0000.0000.0000.000
나트륨(201~300W)(개소)1.0000.4441.0000.8230.0000.0000.0000.000NaN0.5420.8180.627
나트륨(301~400W)(개소)1.0000.0000.8231.0000.0000.0000.0000.000NaN0.4800.0000.314
메탈(0~99W)(개소)1.0000.0000.0000.0001.000NaNNaNNaN1.0000.000NaN0.000
메탈(100~150W)(개소)1.0000.6180.0000.000NaN1.0000.7500.7360.0000.0000.3960.000
메탈(151~300W)(개소)1.0000.0000.0000.000NaN0.7501.0000.623NaN0.0000.0000.000
메탈(301~400W)(개소)1.0000.0000.0000.000NaN0.7360.6231.000NaN0.0000.0000.000
LED(0~49W)(개소)1.0000.000NaNNaN1.0000.000NaNNaN1.0000.538NaNNaN
LED(50~100W)(개소)1.0000.0000.5420.4800.0000.0000.0000.0000.5381.0000.8890.657
LED(101~120W)(개소)1.0000.0000.8180.000NaN0.3960.0000.000NaN0.8891.0000.000
LED(121~150W)(개소)1.0000.0000.6270.3140.0000.0000.0000.000NaN0.6570.0001.000
2024-03-23T01:51:20.587572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
나트륨(301~400W)(개소)메탈(0~99W)(개소)메탈(301~400W)(개소)
나트륨(301~400W)(개소)1.0000.0000.000
메탈(0~99W)(개소)0.0001.0001.000
메탈(301~400W)(개소)0.0001.0001.000
2024-03-23T01:51:20.902113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
나트륨(100~200W)(개소)나트륨(201~300W)(개소)메탈(100~150W)(개소)메탈(151~300W)(개소)LED(0~49W)(개소)LED(50~100W)(개소)LED(101~120W)(개소)LED(121~150W)(개소)나트륨(301~400W)(개소)메탈(0~99W)(개소)메탈(301~400W)(개소)
나트륨(100~200W)(개소)1.0000.069-0.035-0.196-0.783-0.375-0.057-0.3460.0000.0000.000
나트륨(201~300W)(개소)0.0691.0000.0370.4340.0200.2210.2960.3320.5750.0000.000
메탈(100~150W)(개소)-0.0350.0371.0000.0190.546-0.2830.0540.0460.0001.0000.471
메탈(151~300W)(개소)-0.1960.4340.0191.0000.6970.0700.7680.6960.0001.0000.426
LED(0~49W)(개소)-0.7830.0200.5460.6971.0000.3130.5520.5411.0000.8161.000
LED(50~100W)(개소)-0.3750.221-0.2830.0700.3131.0000.1700.1940.5210.0000.000
LED(101~120W)(개소)-0.0570.2960.0540.7680.5520.1701.0000.8800.0001.0000.000
LED(121~150W)(개소)-0.3460.3320.0460.6960.5410.1940.8801.0000.1960.0000.000
나트륨(301~400W)(개소)0.0000.5750.0000.0001.0000.5210.0000.1961.0000.0000.000
메탈(0~99W)(개소)0.0000.0001.0001.0000.8160.0001.0000.0000.0001.0001.000
메탈(301~400W)(개소)0.0000.0000.4710.4261.0000.0000.0000.0000.0001.0001.000

Missing values

2024-03-23T01:51:07.986781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T01:51:08.601872image/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.
2024-03-23T01:51:09.119609image/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

시군명나트륨(100~200W)(개소)나트륨(201~300W)(개소)나트륨(301~400W)(개소)메탈(0~99W)(개소)메탈(100~150W)(개소)메탈(151~300W)(개소)메탈(301~400W)(개소)LED(0~49W)(개소)LED(50~100W)(개소)LED(101~120W)(개소)LED(121~150W)(개소)
0가평군6<NA><NA>6306<NA><NA><NA>29691944<NA><NA>
1고양시336400<NA>1101900<NA>6600
2과천시97901<NA>21800<NA>1363312
3광명시351900<NA>000018900
4광주시000<NA>1289200<NA>42300
5구리시14510<NA>137300<NA>72900
6군포시<NA><NA><NA>50<NA><NA><NA><NA>2250<NA><NA>
7김포시000<NA>346147581123<NA>28414288
8남양주시188995360<NA>12400023900
9부천시12861250<NA>954230<NA>464140
시군명나트륨(100~200W)(개소)나트륨(201~300W)(개소)나트륨(301~400W)(개소)메탈(0~99W)(개소)메탈(100~150W)(개소)메탈(151~300W)(개소)메탈(301~400W)(개소)LED(0~49W)(개소)LED(50~100W)(개소)LED(101~120W)(개소)LED(121~150W)(개소)
17양평군177<NA><NA><NA>25<NA>146034144<NA>
18여주시10162<NA><NA><NA>20<NA><NA>866<NA>169
19연천군<NA>79<NA>302430<NA><NA><NA>2386<NA><NA>
20용인시263734060<NA>1169800<NA>43300
21의왕시102000<NA>105600<NA>8000
22의정부시79600<NA>34500<NA>381900
23이천시<NA><NA><NA><NA><NA><NA><NA><NA>142794268<NA>
24평택시118300<NA>000<NA>8500
25포천시26235910140666759481001
26하남시170000<NA>265300<NA>2000