Overview

Dataset statistics

Number of variables15
Number of observations28
Missing cells113
Missing cells (%)26.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory137.7 B

Variable types

Text2
Categorical1
Numeric12

Alerts

나트륨(301~400W)(개소) is highly overall correlated with 메탈(100W미만)(개소) and 1 other fieldsHigh correlation
메탈(100W미만)(개소) is highly overall correlated with 나트륨(301~400W)(개소) and 3 other fieldsHigh correlation
메탈(100~150W)(개소) is highly overall correlated with LED(151W이상)(개소)High correlation
메탈(301~400W)(개소) is highly overall correlated with 메탈(100W미만)(개소)High correlation
LED(50W미만)(개소) is highly overall correlated with 메탈(100W미만)(개소) and 2 other fieldsHigh correlation
LED(50~100W)(개소) is highly overall correlated with LED(50W미만)(개소) and 2 other fieldsHigh correlation
LED(101~120W)(개소) is highly overall correlated with 메탈(100W미만)(개소)High correlation
LED(121~150W)(개소) is highly overall correlated with LED(50~100W)(개소)High correlation
LED(151W이상)(개소) is highly overall correlated with 메탈(100~150W)(개소) and 1 other fieldsHigh correlation
나트륨(100W미만)(개소) is highly overall correlated with 나트륨(301~400W)(개소) and 3 other fieldsHigh correlation
나트륨(100W미만)(개소) is highly imbalanced (51.2%)Imbalance
나트륨(100~200W)(개소) has 3 (10.7%) missing valuesMissing
나트륨(201~300W)(개소) has 6 (21.4%) missing valuesMissing
나트륨(301~400W)(개소) has 10 (35.7%) missing valuesMissing
메탈(100W미만)(개소) has 17 (60.7%) missing valuesMissing
메탈(100~150W)(개소) has 6 (21.4%) missing valuesMissing
메탈(151~300W)(개소) has 5 (17.9%) missing valuesMissing
메탈(301~400W)(개소) has 11 (39.3%) missing valuesMissing
LED(50W미만)(개소) has 8 (28.6%) missing valuesMissing
LED(50~100W)(개소) has 1 (3.6%) missing valuesMissing
LED(101~120W)(개소) has 5 (17.9%) missing valuesMissing
LED(121~150W)(개소) has 2 (7.1%) missing valuesMissing
LED(151W이상)(개소) has 18 (64.3%) missing valuesMissing
기타 has 21 (75.0%) missing valuesMissing
시군명 has unique valuesUnique
나트륨(100~200W)(개소) has 7 (25.0%) zerosZeros
나트륨(201~300W)(개소) has 2 (7.1%) zerosZeros
나트륨(301~400W)(개소) has 9 (32.1%) zerosZeros
메탈(100W미만)(개소) has 2 (7.1%) zerosZeros
메탈(151~300W)(개소) has 3 (10.7%) zerosZeros
메탈(301~400W)(개소) has 9 (32.1%) zerosZeros
LED(50W미만)(개소) has 10 (35.7%) zerosZeros
LED(50~100W)(개소) has 2 (7.1%) zerosZeros
LED(101~120W)(개소) has 5 (17.9%) zerosZeros
LED(121~150W)(개소) has 2 (7.1%) zerosZeros
LED(151W이상)(개소) has 2 (7.1%) zerosZeros

Reproduction

Analysis started2024-03-23 01:45:03.992941
Analysis finished2024-03-23 01:45:50.593141
Duration46.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-03-23T01:45:50.839649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0714286
Min length3

Characters and Unicode

Total characters86
Distinct characters34
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

Unique28 ?
Unique (%)100.0%

Sample

1st row가평군
2nd row고양시
3rd row광명시
4th row광주시
5th row구리시
ValueCountFrequency (%)
가평군 1
 
3.6%
고양시 1
 
3.6%
포천시 1
 
3.6%
평택시 1
 
3.6%
파주시 1
 
3.6%
이천시 1
 
3.6%
의정부시 1
 
3.6%
의왕시 1
 
3.6%
용인시 1
 
3.6%
오산시 1
 
3.6%
Other values (18) 18
64.3%
2024-03-23T01:45:51.631903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26
30.2%
5
 
5.8%
5
 
5.8%
4
 
4.7%
4
 
4.7%
3
 
3.5%
3
 
3.5%
3
 
3.5%
3
 
3.5%
2
 
2.3%
Other values (24) 28
32.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 86
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
26
30.2%
5
 
5.8%
5
 
5.8%
4
 
4.7%
4
 
4.7%
3
 
3.5%
3
 
3.5%
3
 
3.5%
3
 
3.5%
2
 
2.3%
Other values (24) 28
32.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 86
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
26
30.2%
5
 
5.8%
5
 
5.8%
4
 
4.7%
4
 
4.7%
3
 
3.5%
3
 
3.5%
3
 
3.5%
3
 
3.5%
2
 
2.3%
Other values (24) 28
32.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 86
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
26
30.2%
5
 
5.8%
5
 
5.8%
4
 
4.7%
4
 
4.7%
3
 
3.5%
3
 
3.5%
3
 
3.5%
3
 
3.5%
2
 
2.3%
Other values (24) 28
32.6%

나트륨(100W미만)(개소)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size356.0 B
<NA>
22 
0
67
 
1
380
 
1
1
 
1

Length

Max length4
Median length4
Mean length3.4642857
Min length1

Unique

Unique3 ?
Unique (%)10.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 22
78.6%
0 3
 
10.7%
67 1
 
3.6%
380 1
 
3.6%
1 1
 
3.6%

Length

2024-03-23T01:45:52.012936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T01:45:52.375410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 22
78.6%
0 3
 
10.7%
67 1
 
3.6%
380 1
 
3.6%
1 1
 
3.6%

나트륨(100~200W)(개소)
Real number (ℝ)

MISSING  ZEROS 

Distinct19
Distinct (%)76.0%
Missing3
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean279.72
Minimum0
Maximum2734
Zeros7
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-23T01:45:52.717431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median36
Q3271
95-th percentile935.2
Maximum2734
Range2734
Interquartile range (IQR)271

Descriptive statistics

Standard deviation577.05983
Coefficient of variation (CV)2.0629909
Kurtosis14.299477
Mean279.72
Median Absolute Deviation (MAD)36
Skewness3.5474449
Sum6993
Variance332998.04
MonotonicityNot monotonic
2024-03-23T01:45:53.146851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 7
25.0%
543 1
 
3.6%
79 1
 
3.6%
1 1
 
3.6%
239 1
 
3.6%
946 1
 
3.6%
97 1
 
3.6%
13 1
 
3.6%
34 1
 
3.6%
8 1
 
3.6%
Other values (9) 9
32.1%
(Missing) 3
 
10.7%
ValueCountFrequency (%)
0 7
25.0%
1 1
 
3.6%
8 1
 
3.6%
13 1
 
3.6%
18 1
 
3.6%
34 1
 
3.6%
36 1
 
3.6%
79 1
 
3.6%
97 1
 
3.6%
163 1
 
3.6%
ValueCountFrequency (%)
2734 1
3.6%
946 1
3.6%
892 1
3.6%
543 1
3.6%
417 1
3.6%
329 1
3.6%
271 1
3.6%
239 1
3.6%
173 1
3.6%
163 1
3.6%

나트륨(201~300W)(개소)
Real number (ℝ)

MISSING  ZEROS 

Distinct21
Distinct (%)95.5%
Missing6
Missing (%)21.4%
Infinite0
Infinite (%)0.0%
Mean3169.7727
Minimum0
Maximum16256
Zeros2
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-23T01:45:53.601514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q1308.75
median1808.5
Q33444.75
95-th percentile12808.2
Maximum16256
Range16256
Interquartile range (IQR)3136

Descriptive statistics

Standard deviation4298.5316
Coefficient of variation (CV)1.3561009
Kurtosis4.0084961
Mean3169.7727
Median Absolute Deviation (MAD)1609.5
Skewness2.0825979
Sum69735
Variance18477374
MonotonicityNot monotonic
2024-03-23T01:45:54.104414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 2
 
7.1%
4 1
 
3.6%
1770 1
 
3.6%
1894 1
 
3.6%
8518 1
 
3.6%
1700 1
 
3.6%
1639 1
 
3.6%
16256 1
 
3.6%
365 1
 
3.6%
108 1
 
3.6%
Other values (11) 11
39.3%
(Missing) 6
21.4%
ValueCountFrequency (%)
0 2
7.1%
4 1
3.6%
40 1
3.6%
108 1
3.6%
290 1
3.6%
365 1
3.6%
1639 1
3.6%
1665 1
3.6%
1700 1
3.6%
1770 1
3.6%
ValueCountFrequency (%)
16256 1
3.6%
13034 1
3.6%
8518 1
3.6%
6053 1
3.6%
4307 1
3.6%
3821 1
3.6%
2316 1
3.6%
2239 1
3.6%
1894 1
3.6%
1869 1
3.6%

나트륨(301~400W)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)55.6%
Missing10
Missing (%)35.7%
Infinite0
Infinite (%)0.0%
Mean1041.7778
Minimum0
Maximum13202
Zeros9
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-23T01:45:54.524924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q3311.5
95-th percentile4884.75
Maximum13202
Range13202
Interquartile range (IQR)311.5

Descriptive statistics

Standard deviation3142.9043
Coefficient of variation (CV)3.0168663
Kurtosis15.212184
Mean1041.7778
Median Absolute Deviation (MAD)1
Skewness3.830803
Sum18752
Variance9877847.4
MonotonicityNot monotonic
2024-03-23T01:45:54.986709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 9
32.1%
280 1
 
3.6%
3417 1
 
3.6%
1090 1
 
3.6%
81 1
 
3.6%
2 1
 
3.6%
336 1
 
3.6%
322 1
 
3.6%
13202 1
 
3.6%
22 1
 
3.6%
(Missing) 10
35.7%
ValueCountFrequency (%)
0 9
32.1%
2 1
 
3.6%
22 1
 
3.6%
81 1
 
3.6%
280 1
 
3.6%
322 1
 
3.6%
336 1
 
3.6%
1090 1
 
3.6%
3417 1
 
3.6%
13202 1
 
3.6%
ValueCountFrequency (%)
13202 1
 
3.6%
3417 1
 
3.6%
1090 1
 
3.6%
336 1
 
3.6%
322 1
 
3.6%
280 1
 
3.6%
81 1
 
3.6%
22 1
 
3.6%
2 1
 
3.6%
0 9
32.1%

메탈(100W미만)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)90.9%
Missing17
Missing (%)60.7%
Infinite0
Infinite (%)0.0%
Mean984.36364
Minimum0
Maximum6421
Zeros2
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-23T01:45:55.630586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median113
Q31040.5
95-th percentile4164.5
Maximum6421
Range6421
Interquartile range (IQR)1030.5

Descriptive statistics

Standard deviation1922.1244
Coefficient of variation (CV)1.9526568
Kurtosis7.6500806
Mean984.36364
Median Absolute Deviation (MAD)113
Skewness2.6835179
Sum10828
Variance3694562.1
MonotonicityNot monotonic
2024-03-23T01:45:56.089582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 2
 
7.1%
6421 1
 
3.6%
1908 1
 
3.6%
1542 1
 
3.6%
17 1
 
3.6%
113 1
 
3.6%
63 1
 
3.6%
222 1
 
3.6%
3 1
 
3.6%
539 1
 
3.6%
(Missing) 17
60.7%
ValueCountFrequency (%)
0 2
7.1%
3 1
3.6%
17 1
3.6%
63 1
3.6%
113 1
3.6%
222 1
3.6%
539 1
3.6%
1542 1
3.6%
1908 1
3.6%
6421 1
3.6%
ValueCountFrequency (%)
6421 1
3.6%
1908 1
3.6%
1542 1
3.6%
539 1
3.6%
222 1
3.6%
113 1
3.6%
63 1
3.6%
17 1
3.6%
3 1
3.6%
0 2
7.1%

메탈(100~150W)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing6
Missing (%)21.4%
Infinite0
Infinite (%)0.0%
Mean3986.4091
Minimum22
Maximum12969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-23T01:45:56.598756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile28.3
Q1221.75
median3507.5
Q36575.5
95-th percentile12191.4
Maximum12969
Range12947
Interquartile range (IQR)6353.75

Descriptive statistics

Standard deviation3993.9201
Coefficient of variation (CV)1.0018841
Kurtosis0.031300309
Mean3986.4091
Median Absolute Deviation (MAD)3332
Skewness0.89240861
Sum87701
Variance15951398
MonotonicityNot monotonic
2024-03-23T01:45:57.088380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2950 1
 
3.6%
389 1
 
3.6%
56 1
 
3.6%
12358 1
 
3.6%
1300 1
 
3.6%
9026 1
 
3.6%
2133 1
 
3.6%
6963 1
 
3.6%
1249 1
 
3.6%
59 1
 
3.6%
Other values (12) 12
42.9%
(Missing) 6
21.4%
ValueCountFrequency (%)
22 1
3.6%
27 1
3.6%
53 1
3.6%
56 1
3.6%
59 1
3.6%
166 1
3.6%
389 1
3.6%
1249 1
3.6%
1300 1
3.6%
2133 1
3.6%
ValueCountFrequency (%)
12969 1
3.6%
12358 1
3.6%
9026 1
3.6%
7560 1
3.6%
6963 1
3.6%
6830 1
3.6%
5812 1
3.6%
4962 1
3.6%
4432 1
3.6%
4320 1
3.6%

메탈(151~300W)(개소)
Real number (ℝ)

MISSING  ZEROS 

Distinct21
Distinct (%)91.3%
Missing5
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean2352.1739
Minimum0
Maximum16627
Zeros3
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-23T01:45:57.462410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1108.5
median1030
Q32699.5
95-th percentile6571.5
Maximum16627
Range16627
Interquartile range (IQR)2591

Descriptive statistics

Standard deviation3754.2226
Coefficient of variation (CV)1.5960651
Kurtosis9.3514468
Mean2352.1739
Median Absolute Deviation (MAD)1020
Skewness2.8133672
Sum54100
Variance14094187
MonotonicityNot monotonic
2024-03-23T01:45:57.848602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 3
 
10.7%
132 1
 
3.6%
1347 1
 
3.6%
312 1
 
3.6%
6580 1
 
3.6%
85 1
 
3.6%
686 1
 
3.6%
1517 1
 
3.6%
245 1
 
3.6%
1049 1
 
3.6%
Other values (11) 11
39.3%
(Missing) 5
17.9%
ValueCountFrequency (%)
0 3
10.7%
10 1
 
3.6%
63 1
 
3.6%
85 1
 
3.6%
132 1
 
3.6%
245 1
 
3.6%
312 1
 
3.6%
686 1
 
3.6%
849 1
 
3.6%
1030 1
 
3.6%
ValueCountFrequency (%)
16627 1
3.6%
6580 1
3.6%
6495 1
3.6%
6094 1
3.6%
3222 1
3.6%
2898 1
3.6%
2501 1
3.6%
2358 1
3.6%
1517 1
3.6%
1347 1
3.6%

메탈(301~400W)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)47.1%
Missing11
Missing (%)39.3%
Infinite0
Infinite (%)0.0%
Mean138.17647
Minimum0
Maximum1206
Zeros9
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-23T01:45:58.226372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q333
95-th percentile970.8
Maximum1206
Range1206
Interquartile range (IQR)33

Descriptive statistics

Standard deviation351.9402
Coefficient of variation (CV)2.5470342
Kurtosis6.215223
Mean138.17647
Median Absolute Deviation (MAD)0
Skewness2.6787523
Sum2349
Variance123861.9
MonotonicityNot monotonic
2024-03-23T01:45:58.574965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 9
32.1%
1 2
 
7.1%
33 1
 
3.6%
30 1
 
3.6%
1206 1
 
3.6%
912 1
 
3.6%
130 1
 
3.6%
36 1
 
3.6%
(Missing) 11
39.3%
ValueCountFrequency (%)
0 9
32.1%
1 2
 
7.1%
30 1
 
3.6%
33 1
 
3.6%
36 1
 
3.6%
130 1
 
3.6%
912 1
 
3.6%
1206 1
 
3.6%
ValueCountFrequency (%)
1206 1
 
3.6%
912 1
 
3.6%
130 1
 
3.6%
36 1
 
3.6%
33 1
 
3.6%
30 1
 
3.6%
1 2
 
7.1%
0 9
32.1%

LED(50W미만)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)55.0%
Missing8
Missing (%)28.6%
Infinite0
Infinite (%)0.0%
Mean409.9
Minimum0
Maximum2876
Zeros10
Zeros (%)35.7%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-23T01:45:58.950863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.5
Q3195
95-th percentile2523.55
Maximum2876
Range2876
Interquartile range (IQR)195

Descriptive statistics

Standard deviation845.67481
Coefficient of variation (CV)2.0631247
Kurtosis4.5179596
Mean409.9
Median Absolute Deviation (MAD)5.5
Skewness2.3137264
Sum8198
Variance715165.88
MonotonicityNot monotonic
2024-03-23T01:45:59.365095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 10
35.7%
2505 1
 
3.6%
66 1
 
3.6%
294 1
 
3.6%
104 1
 
3.6%
67 1
 
3.6%
162 1
 
3.6%
2876 1
 
3.6%
920 1
 
3.6%
11 1
 
3.6%
(Missing) 8
28.6%
ValueCountFrequency (%)
0 10
35.7%
11 1
 
3.6%
66 1
 
3.6%
67 1
 
3.6%
104 1
 
3.6%
162 1
 
3.6%
294 1
 
3.6%
920 1
 
3.6%
1193 1
 
3.6%
2505 1
 
3.6%
ValueCountFrequency (%)
2876 1
3.6%
2505 1
3.6%
1193 1
3.6%
920 1
3.6%
294 1
3.6%
162 1
3.6%
104 1
3.6%
67 1
3.6%
66 1
3.6%
11 1
3.6%

LED(50~100W)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct26
Distinct (%)96.3%
Missing1
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean2762.5926
Minimum0
Maximum20229
Zeros2
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-23T01:45:59.734694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.8
Q1408.5
median1319
Q32420.5
95-th percentile11167.8
Maximum20229
Range20229
Interquartile range (IQR)2012

Descriptive statistics

Standard deviation4445.1437
Coefficient of variation (CV)1.6090478
Kurtosis9.4482268
Mean2762.5926
Median Absolute Deviation (MAD)982
Skewness2.9522327
Sum74590
Variance19759302
MonotonicityNot monotonic
2024-03-23T01:46:00.189870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 2
 
7.1%
2100 1
 
3.6%
831 1
 
3.6%
1996 1
 
3.6%
2154 1
 
3.6%
590 1
 
3.6%
1007 1
 
3.6%
175 1
 
3.6%
480 1
 
3.6%
8050 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
0 2
7.1%
66 1
3.6%
175 1
3.6%
179 1
3.6%
189 1
3.6%
337 1
3.6%
480 1
3.6%
590 1
3.6%
831 1
3.6%
1007 1
3.6%
ValueCountFrequency (%)
20229 1
3.6%
12504 1
3.6%
8050 1
3.6%
5497 1
3.6%
3722 1
3.6%
2914 1
3.6%
2687 1
3.6%
2154 1
3.6%
2114 1
3.6%
2100 1
3.6%

LED(101~120W)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)82.6%
Missing5
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean937.82609
Minimum0
Maximum5745
Zeros5
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-23T01:46:00.552952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.5
median111
Q3721.5
95-th percentile4941.8
Maximum5745
Range5745
Interquartile range (IQR)706

Descriptive statistics

Standard deviation1681.3062
Coefficient of variation (CV)1.7927697
Kurtosis3.2641128
Mean937.82609
Median Absolute Deviation (MAD)111
Skewness2.0619367
Sum21570
Variance2826790.6
MonotonicityNot monotonic
2024-03-23T01:46:00.916953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 5
17.9%
26 1
 
3.6%
422 1
 
3.6%
1012 1
 
3.6%
111 1
 
3.6%
3446 1
 
3.6%
41 1
 
3.6%
363 1
 
3.6%
159 1
 
3.6%
322 1
 
3.6%
Other values (9) 9
32.1%
(Missing) 5
17.9%
ValueCountFrequency (%)
0 5
17.9%
5 1
 
3.6%
26 1
 
3.6%
41 1
 
3.6%
88 1
 
3.6%
101 1
 
3.6%
106 1
 
3.6%
111 1
 
3.6%
159 1
 
3.6%
322 1
 
3.6%
ValueCountFrequency (%)
5745 1
3.6%
5108 1
3.6%
3446 1
3.6%
2883 1
3.6%
1201 1
3.6%
1012 1
3.6%
431 1
3.6%
422 1
3.6%
363 1
3.6%
322 1
3.6%

LED(121~150W)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)96.2%
Missing2
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean2761.1923
Minimum0
Maximum10586
Zeros2
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-23T01:46:01.332541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q1269.5
median1619
Q34472.75
95-th percentile8188.5
Maximum10586
Range10586
Interquartile range (IQR)4203.25

Descriptive statistics

Standard deviation3037.2677
Coefficient of variation (CV)1.0999841
Kurtosis0.34784329
Mean2761.1923
Median Absolute Deviation (MAD)1538
Skewness1.1183435
Sum71791
Variance9224995
MonotonicityNot monotonic
2024-03-23T01:46:01.690463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 2
 
7.1%
1319 1
 
3.6%
1260 1
 
3.6%
1919 1
 
3.6%
295 1
 
3.6%
243 1
 
3.6%
2011 1
 
3.6%
470 1
 
3.6%
462 1
 
3.6%
1314 1
 
3.6%
Other values (15) 15
53.6%
(Missing) 2
 
7.1%
ValueCountFrequency (%)
0 2
7.1%
64 1
3.6%
68 1
3.6%
94 1
3.6%
243 1
3.6%
261 1
3.6%
295 1
3.6%
462 1
3.6%
470 1
3.6%
1260 1
3.6%
ValueCountFrequency (%)
10586 1
3.6%
8425 1
3.6%
7479 1
3.6%
7260 1
3.6%
5711 1
3.6%
5108 1
3.6%
4672 1
3.6%
3875 1
3.6%
3836 1
3.6%
3103 1
3.6%

LED(151W이상)(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)90.0%
Missing18
Missing (%)64.3%
Infinite0
Infinite (%)0.0%
Mean119.9
Minimum0
Maximum568
Zeros2
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-03-23T01:46:01.974724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.75
median28
Q3141
95-th percentile442
Maximum568
Range568
Interquartile range (IQR)134.25

Descriptive statistics

Standard deviation182.86999
Coefficient of variation (CV)1.5251876
Kurtosis3.8588746
Mean119.9
Median Absolute Deviation (MAD)28
Skewness1.9718565
Sum1199
Variance33441.433
MonotonicityNot monotonic
2024-03-23T01:46:02.296010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 2
 
7.1%
146 1
 
3.6%
568 1
 
3.6%
126 1
 
3.6%
288 1
 
3.6%
40 1
 
3.6%
9 1
 
3.6%
6 1
 
3.6%
16 1
 
3.6%
(Missing) 18
64.3%
ValueCountFrequency (%)
0 2
7.1%
6 1
3.6%
9 1
3.6%
16 1
3.6%
40 1
3.6%
126 1
3.6%
146 1
3.6%
288 1
3.6%
568 1
3.6%
ValueCountFrequency (%)
568 1
3.6%
288 1
3.6%
146 1
3.6%
126 1
3.6%
40 1
3.6%
16 1
3.6%
9 1
3.6%
6 1
3.6%
0 2
7.1%

기타
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing21
Missing (%)75.0%
Memory size356.0 B
2024-03-23T01:46:02.640928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1428571
Min length1

Characters and Unicode

Total characters22
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st row226
2nd row2,796
3rd row0
4th row1,267
5th row17
ValueCountFrequency (%)
226 1
14.3%
2,796 1
14.3%
0 1
14.3%
1,267 1
14.3%
17 1
14.3%
536 1
14.3%
264 1
14.3%
2024-03-23T01:46:03.402199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 5
22.7%
6 5
22.7%
7 3
13.6%
, 2
 
9.1%
1 2
 
9.1%
9 1
 
4.5%
0 1
 
4.5%
5 1
 
4.5%
3 1
 
4.5%
4 1
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20
90.9%
Other Punctuation 2
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5
25.0%
6 5
25.0%
7 3
15.0%
1 2
 
10.0%
9 1
 
5.0%
0 1
 
5.0%
5 1
 
5.0%
3 1
 
5.0%
4 1
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5
22.7%
6 5
22.7%
7 3
13.6%
, 2
 
9.1%
1 2
 
9.1%
9 1
 
4.5%
0 1
 
4.5%
5 1
 
4.5%
3 1
 
4.5%
4 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5
22.7%
6 5
22.7%
7 3
13.6%
, 2
 
9.1%
1 2
 
9.1%
9 1
 
4.5%
0 1
 
4.5%
5 1
 
4.5%
3 1
 
4.5%
4 1
 
4.5%

Interactions

2024-03-23T01:45:44.838541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:05.115133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:08.745914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:12.404314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:15.918571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:19.323183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:23.004426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:26.646810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:30.310625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:33.760247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:37.321343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:40.702844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:45.090275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:05.386540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:09.070380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:12.667874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:16.195396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:19.679818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:23.297275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:26.973516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:30.600561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:34.028034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:37.552006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:40.976453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:45.373052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:05.647267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:09.386266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:12.944937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:16.472570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:19.989406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:23.622100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:27.269572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:30.908498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:34.379491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:37.806547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:41.318996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:45.769866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:05.890479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:09.710745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:13.204340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:16.739542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:20.281396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:23.904800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:27.540568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:31.172626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:34.651222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:38.072504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:41.622488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:46.087731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:06.178833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:10.100243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:13.532637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:17.016181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:20.571038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:24.241502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:27.840095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:31.422841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:34.934706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:38.360774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:42.008118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:46.368275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:06.430516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:10.556281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:13.777897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:17.292605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:20.922236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:24.515834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:28.105949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:31.683508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:35.330705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:38.599782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:42.337167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:46.600611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:06.762418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:10.811410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:14.069628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:17.550750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:21.230086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:24.809811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:28.566135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:31.945589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:35.688288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:38.930216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:42.680321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:46.939393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:07.011218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:11.114384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:14.318305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:17.824541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:21.521342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:25.119660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:28.866254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:32.434843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:35.959358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:39.190930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:43.106823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:47.205457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:07.367208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:11.367550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:14.623920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:18.067582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:21.795568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:25.480833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:29.154425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:32.705606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:36.202634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:39.476161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:43.389360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:47.471377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:07.746045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:11.652810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:14.943220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:18.422467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:22.155725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:25.820933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:29.512949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:33.008560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:36.506122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:39.745498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:43.734832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:47.743515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:08.052128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:11.910389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:15.220380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:18.689737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:22.469907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:26.092137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:29.814596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:33.244777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:36.805413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:40.014301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:44.177794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:48.038990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:08.412618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:12.156937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:15.450072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:19.029962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:22.740849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:26.359732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:30.043267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:33.472587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:37.086214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:40.237991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:45:44.576670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T01:46:03.761429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명나트륨(100W미만)(개소)나트륨(100~200W)(개소)나트륨(201~300W)(개소)나트륨(301~400W)(개소)메탈(100W미만)(개소)메탈(100~150W)(개소)메탈(151~300W)(개소)메탈(301~400W)(개소)LED(50W미만)(개소)LED(50~100W)(개소)LED(101~120W)(개소)LED(121~150W)(개소)LED(151W이상)(개소)기타
시군명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
나트륨(100W미만)(개소)1.0001.0000.0000.000NaN0.0000.1650.8340.0001.0001.0000.8340.4161.0001.000
나트륨(100~200W)(개소)1.0000.0001.0000.6910.0000.0000.0000.6630.0000.0000.3020.0000.7760.0001.000
나트륨(201~300W)(개소)1.0000.0000.6911.0000.0000.0000.8580.3630.0000.1980.0000.6590.0000.0001.000
나트륨(301~400W)(개소)1.000NaN0.0000.0001.000NaN0.0000.3230.0000.0000.0000.0000.000NaNNaN
메탈(100W미만)(개소)1.0000.0000.0000.000NaN1.0000.8400.1811.0000.4870.5681.0001.0001.0001.000
메탈(100~150W)(개소)1.0000.1650.0000.8580.0000.8401.0000.8850.0000.7180.0000.8070.0000.7441.000
메탈(151~300W)(개소)1.0000.8340.6630.3630.3230.1810.8851.0000.3280.4040.4440.7570.6790.5911.000
메탈(301~400W)(개소)1.0000.0000.0000.0000.0001.0000.0000.3281.0000.0000.7990.8630.9851.0001.000
LED(50W미만)(개소)1.0001.0000.0000.1980.0000.4870.7180.4040.0001.0000.6290.6000.4850.5681.000
LED(50~100W)(개소)1.0001.0000.3020.0000.0000.5680.0000.4440.7990.6291.0000.7850.8060.8771.000
LED(101~120W)(개소)1.0000.8340.0000.6590.0001.0000.8070.7570.8630.6000.7851.0000.5930.8631.000
LED(121~150W)(개소)1.0000.4160.7760.0000.0001.0000.0000.6790.9850.4850.8060.5931.0000.8971.000
LED(151W이상)(개소)1.0001.0000.0000.000NaN1.0000.7440.5911.0000.5680.8770.8630.8971.0001.000
기타1.0001.0001.0001.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-03-23T01:46:04.198909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
나트륨(100~200W)(개소)나트륨(201~300W)(개소)나트륨(301~400W)(개소)메탈(100W미만)(개소)메탈(100~150W)(개소)메탈(151~300W)(개소)메탈(301~400W)(개소)LED(50W미만)(개소)LED(50~100W)(개소)LED(101~120W)(개소)LED(121~150W)(개소)LED(151W이상)(개소)나트륨(100W미만)(개소)
나트륨(100~200W)(개소)1.0000.424-0.1350.1610.0260.3290.272-0.1450.2980.0120.348-0.0640.000
나트륨(201~300W)(개소)0.4241.0000.1800.0550.3510.308-0.162-0.281-0.2270.354-0.378-0.1580.000
나트륨(301~400W)(개소)-0.1350.1801.0000.625-0.2470.2150.293-0.182-0.4460.119-0.4520.3571.000
메탈(100W미만)(개소)0.1610.0550.6251.000-0.0060.0120.7840.527-0.1690.634-0.1280.2350.000
메탈(100~150W)(개소)0.0260.351-0.247-0.0061.0000.228-0.247-0.243-0.159-0.159-0.2830.5030.000
메탈(151~300W)(개소)0.3290.3080.2150.0120.2281.0000.1200.1180.2050.0010.2480.4130.333
메탈(301~400W)(개소)0.272-0.1620.2930.784-0.2470.1201.0000.0090.1790.0900.3990.3970.000
LED(50W미만)(개소)-0.145-0.281-0.1820.527-0.2430.1180.0091.0000.5920.2660.4060.2430.816
LED(50~100W)(개소)0.298-0.227-0.446-0.169-0.1590.2050.1790.5921.0000.2090.6460.2920.816
LED(101~120W)(개소)0.0120.3540.1190.634-0.1590.0010.0900.2660.2091.0000.0070.0640.333
LED(121~150W)(개소)0.348-0.378-0.452-0.128-0.2830.2480.3990.4060.6460.0071.0000.2680.000
LED(151W이상)(개소)-0.064-0.1580.3570.2350.5030.4130.3970.2430.2920.0640.2681.0000.816
나트륨(100W미만)(개소)0.0000.0001.0000.0000.0000.3330.0000.8160.8160.3330.0000.8161.000

Missing values

2024-03-23T01:45:48.479383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T01:45:49.295902image/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.
2024-03-23T01:45:50.135878image/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(100W미만)(개소)나트륨(100~200W)(개소)나트륨(201~300W)(개소)나트륨(301~400W)(개소)메탈(100W미만)(개소)메탈(100~150W)(개소)메탈(151~300W)(개소)메탈(301~400W)(개소)LED(50W미만)(개소)LED(50~100W)(개소)LED(101~120W)(개소)LED(121~150W)(개소)LED(151W이상)(개소)기타
0가평군<NA>8<NA><NA>6421<NA><NA><NA>25052100<NA><NA><NA><NA>
1고양시<NA>32938210<NA>581216627<NA><NA>1319<NA>7479<NA><NA>
2광명시<NA>181869<NA><NA><NA>10<NA>6610093221319146226
3광주시<NA>000<NA>6830000337094<NA><NA>
4구리시<NA>163290280<NA>5363330107003875<NA><NA>
5군포시67173<NA><NA><NA>166<NA><NA><NA>1873<NA>4672<NA>2,796
6김포시<NA>3622393417<NA>756023583006610164<NA><NA>
7남양주시<NA>000012969609402942022907260568<NA>
8부천시02734430700443232220037220195600
9성남시<NA>417130341090<NA>40658490017910668<NA><NA>
시군명나트륨(100W미만)(개소)나트륨(100~200W)(개소)나트륨(201~300W)(개소)나트륨(301~400W)(개소)메탈(100W미만)(개소)메탈(100~150W)(개소)메탈(151~300W)(개소)메탈(301~400W)(개소)LED(50W미만)(개소)LED(50~100W)(개소)LED(101~120W)(개소)LED(121~150W)(개소)LED(151W이상)(개소)기타
18연천군<NA>97365<NA>2221249245<NA><NA>2687159261<NA><NA>
19오산시<NA><NA><NA><NA><NA><NA><NA><NA><NA>80503631314<NA><NA>
20용인시<NA>946162560<NA>696315170048041462<NA><NA>
21의왕시<NA>01639322<NA>21336863601750470<NA><NA>
22의정부시<NA>239<NA><NA><NA>902685<NA><NA>1007<NA>2011<NA><NA>
23이천시<NA><NA><NA><NA><NA><NA><NA><NA>05903446243<NA><NA>
24파주시<NA>0170013202<NA>13006580000111295<NA><NA>
25평택시<NA>085180<NA>12358000010120<NA><NA>
26포천시<NA>11894<NA>356312<NA>11215442219196<NA>
27하남시<NA>79177022539389134711193199626126016<NA>