Overview

Dataset statistics

Number of variables8
Number of observations24
Missing cells61
Missing cells (%)31.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory76.3 B

Variable types

Text1
Numeric7

Dataset

Description2021년말 기준, 대덕, 광주, 대구, 부산, 전북연구개발 특구의 (연구개발특구법상)토지용도별 특구면적 정보입니다. 지구 구분과 주거구역, 상업구역, 녹지구역, 산업육성구역, 산업지원구역, 교육연구 및 사업화 시설구역, 산업공공시설구역 및 산업복합구역으로 구분됩니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15120299/fileData.do

Alerts

주거구역 is highly overall correlated with 상업구역 and 2 other fieldsHigh correlation
상업구역 is highly overall correlated with 주거구역 and 1 other fieldsHigh correlation
녹지구역 is highly overall correlated with 주거구역 and 1 other fieldsHigh correlation
산업육성구역 is highly overall correlated with 산업지원구역High correlation
산업지원구역 is highly overall correlated with 산업육성구역High correlation
교육 연구 및 사업화 시설구역 is highly overall correlated with 주거구역 and 2 other fieldsHigh correlation
주거구역 has 8 (33.3%) missing valuesMissing
상업구역 has 12 (50.0%) missing valuesMissing
산업육성구역 has 7 (29.2%) missing valuesMissing
산업지원구역 has 9 (37.5%) missing valuesMissing
교육 연구 및 사업화 시설구역 has 11 (45.8%) missing valuesMissing
산업공공 시설구역 및 산업복합구역 has 14 (58.3%) missing valuesMissing
지구 has unique valuesUnique
녹지구역 has unique valuesUnique

Reproduction

Analysis started2024-04-21 12:04:01.858258
Analysis finished2024-04-21 12:04:15.369195
Duration13.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지구
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size320.0 B
2024-04-21T21:04:15.876408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length8.25
Min length5

Characters and Unicode

Total characters198
Distinct characters62
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row대덕 제Ⅰ지구
2nd row대덕 제Ⅱ지구
3rd row대덕 제Ⅲ지구
4th row대덕 제Ⅳ지구
5th row대덕 제Ⅴ지구
ValueCountFrequency (%)
광주 7
 
12.7%
대덕 5
 
9.1%
대구 5
 
9.1%
부산 4
 
7.3%
전북 3
 
5.5%
ii 2
 
3.6%
i 2
 
3.6%
첨단복합 1
 
1.8%
생산거점 1
 
1.8%
융합r&d 1
 
1.8%
Other values (24) 24
43.6%
2024-04-21T21:04:16.974334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31
 
15.7%
구 13
 
6.6%
대 11
 
5.6%
지 9
 
4.5%
주 7
 
3.5%
광 7
 
3.5%
I 6
 
3.0%
산 6
 
3.0%
부 5
 
2.5%
제 5
 
2.5%
Other values (52) 98
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 141
71.2%
Space Separator 31
 
15.7%
Uppercase Letter 14
 
7.1%
Letter Number 5
 
2.5%
Other Punctuation 4
 
2.0%
Decimal Number 3
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 13
 
9.2%
대 11
 
7.8%
지 9
 
6.4%
주 7
 
5.0%
광 7
 
5.0%
산 6
 
4.3%
부 5
 
3.5%
제 5
 
3.5%
첨 5
 
3.5%
단 5
 
3.5%
Other values (39) 68
48.2%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
20.0%
Ⅰ 1
20.0%
Ⅲ 1
20.0%
Ⅴ 1
20.0%
Ⅳ 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
I 6
42.9%
R 4
28.6%
D 4
28.6%
Decimal Number
ValueCountFrequency (%)
3 1
33.3%
2 1
33.3%
1 1
33.3%
Space Separator
ValueCountFrequency (%)
31
100.0%
Other Punctuation
ValueCountFrequency (%)
& 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 141
71.2%
Common 38
 
19.2%
Latin 19
 
9.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 13
 
9.2%
대 11
 
7.8%
지 9
 
6.4%
주 7
 
5.0%
광 7
 
5.0%
산 6
 
4.3%
부 5
 
3.5%
제 5
 
3.5%
첨 5
 
3.5%
단 5
 
3.5%
Other values (39) 68
48.2%
Latin
ValueCountFrequency (%)
I 6
31.6%
R 4
21.1%
D 4
21.1%
Ⅱ 1
 
5.3%
Ⅰ 1
 
5.3%
Ⅲ 1
 
5.3%
Ⅴ 1
 
5.3%
Ⅳ 1
 
5.3%
Common
ValueCountFrequency (%)
31
81.6%
& 4
 
10.5%
3 1
 
2.6%
2 1
 
2.6%
1 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 141
71.2%
ASCII 52
 
26.3%
Number Forms 5
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31
59.6%
I 6
 
11.5%
& 4
 
7.7%
R 4
 
7.7%
D 4
 
7.7%
3 1
 
1.9%
2 1
 
1.9%
1 1
 
1.9%
Hangul
ValueCountFrequency (%)
구 13
 
9.2%
대 11
 
7.8%
지 9
 
6.4%
주 7
 
5.0%
광 7
 
5.0%
산 6
 
4.3%
부 5
 
3.5%
제 5
 
3.5%
첨 5
 
3.5%
단 5
 
3.5%
Other values (39) 68
48.2%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
20.0%
Ⅰ 1
20.0%
Ⅲ 1
20.0%
Ⅴ 1
20.0%
Ⅳ 1
20.0%

주거구역
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)100.0%
Missing8
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean547.7625
Minimum7.1
Maximum2677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-04-21T21:04:17.336144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.1
5-th percentile9.95
Q158
median191.75
Q3590.45
95-th percentile1994.725
Maximum2677
Range2669.9
Interquartile range (IQR)532.45

Descriptive statistics

Standard deviation783.23355
Coefficient of variation (CV)1.429878
Kurtosis2.5980808
Mean547.7625
Median Absolute Deviation (MAD)172.5
Skewness1.7798974
Sum8764.2
Variance613454.79
MonotonicityNot monotonic
2024-04-21T21:04:17.692630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1327.5 1
 
4.2%
213.1 1
 
4.2%
329.5 1
 
4.2%
1767.3 1
 
4.2%
361.0 1
 
4.2%
43.0 1
 
4.2%
7.1 1
 
4.2%
2677.0 1
 
4.2%
1211.0 1
 
4.2%
106.8 1
 
4.2%
Other values (6) 6
25.0%
(Missing) 8
33.3%
ValueCountFrequency (%)
7.1 1
4.2%
10.9 1
4.2%
16.0 1
4.2%
43.0 1
4.2%
63.0 1
4.2%
77.0 1
4.2%
106.8 1
4.2%
170.4 1
4.2%
213.1 1
4.2%
329.5 1
4.2%
ValueCountFrequency (%)
2677.0 1
4.2%
1767.3 1
4.2%
1327.5 1
4.2%
1211.0 1
4.2%
383.6 1
4.2%
361.0 1
4.2%
329.5 1
4.2%
213.1 1
4.2%
170.4 1
4.2%
106.8 1
4.2%

상업구역
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)91.7%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean142.775
Minimum8.2
Maximum547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-04-21T21:04:18.287397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.2
5-th percentile14.69
Q138.425
median74
Q3159.65
95-th percentile451.3
Maximum547
Range538.8
Interquartile range (IQR)121.225

Descriptive statistics

Standard deviation163.32819
Coefficient of variation (CV)1.1439551
Kurtosis2.7062666
Mean142.775
Median Absolute Deviation (MAD)57.5
Skewness1.7670674
Sum1713.3
Variance26676.098
MonotonicityNot monotonic
2024-04-21T21:04:18.638001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
74.0 2
 
8.3%
547.0 1
 
4.2%
373.0 1
 
4.2%
34.0 1
 
4.2%
56.0 1
 
4.2%
143.2 1
 
4.2%
8.2 1
 
4.2%
135.0 1
 
4.2%
20.0 1
 
4.2%
209.0 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
8.2 1
4.2%
20.0 1
4.2%
34.0 1
4.2%
39.9 1
4.2%
56.0 1
4.2%
74.0 2
8.3%
135.0 1
4.2%
143.2 1
4.2%
209.0 1
4.2%
373.0 1
4.2%
ValueCountFrequency (%)
547.0 1
4.2%
373.0 1
4.2%
209.0 1
4.2%
143.2 1
4.2%
135.0 1
4.2%
74.0 2
8.3%
56.0 1
4.2%
39.9 1
4.2%
34.0 1
4.2%
20.0 1
4.2%

녹지구역
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1858.4875
Minimum14
Maximum10478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-04-21T21:04:18.993335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile28.65
Q1138.725
median743.35
Q32176.325
95-th percentile9511.7
Maximum10478
Range10464
Interquartile range (IQR)2037.6

Descriptive statistics

Standard deviation2925.3487
Coefficient of variation (CV)1.5740481
Kurtosis5.1097246
Mean1858.4875
Median Absolute Deviation (MAD)658.45
Skewness2.3397298
Sum44603.7
Variance8557664.9
MonotonicityNot monotonic
2024-04-21T21:04:19.370434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10436.0 1
 
4.2%
21.0 1
 
4.2%
145.1 1
 
4.2%
385.5 1
 
4.2%
1947.2 1
 
4.2%
4274.0 1
 
4.2%
14.0 1
 
4.2%
799.9 1
 
4.2%
72.0 1
 
4.2%
2863.7 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
14.0 1
4.2%
21.0 1
4.2%
72.0 1
4.2%
73.8 1
4.2%
96.0 1
4.2%
119.6 1
4.2%
145.1 1
4.2%
168.6 1
4.2%
191.5 1
4.2%
385.5 1
4.2%
ValueCountFrequency (%)
10478.0 1
4.2%
10436.0 1
4.2%
4274.0 1
4.2%
3628.6 1
4.2%
3276.6 1
4.2%
2863.7 1
4.2%
1947.2 1
4.2%
1433.4 1
4.2%
1327.4 1
4.2%
901.0 1
4.2%

산업육성구역
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing7
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean1425.6882
Minimum110.5
Maximum3089.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-04-21T21:04:19.710548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110.5
5-th percentile230.1
Q1596
median1276.7
Q32022.7
95-th percentile2944.3
Maximum3089.5
Range2979
Interquartile range (IQR)1426.7

Descriptive statistics

Standard deviation985.44835
Coefficient of variation (CV)0.69120887
Kurtosis-1.2593252
Mean1425.6882
Median Absolute Deviation (MAD)746
Skewness0.37572603
Sum24236.7
Variance971108.45
MonotonicityNot monotonic
2024-04-21T21:04:20.076257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1876.0 1
 
4.2%
487.1 1
 
4.2%
2811.4 1
 
4.2%
524.0 1
 
4.2%
1974.0 1
 
4.2%
3089.5 1
 
4.2%
697.0 1
 
4.2%
1521.1 1
 
4.2%
660.0 1
 
4.2%
260.0 1
 
4.2%
Other values (7) 7
29.2%
(Missing) 7
29.2%
ValueCountFrequency (%)
110.5 1
4.2%
260.0 1
4.2%
487.1 1
4.2%
524.0 1
4.2%
596.0 1
4.2%
660.0 1
4.2%
697.0 1
4.2%
1000.9 1
4.2%
1276.7 1
4.2%
1521.1 1
4.2%
ValueCountFrequency (%)
3089.5 1
4.2%
2908.0 1
4.2%
2811.4 1
4.2%
2421.8 1
4.2%
2022.7 1
4.2%
1974.0 1
4.2%
1876.0 1
4.2%
1521.1 1
4.2%
1276.7 1
4.2%
1000.9 1
4.2%

산업지원구역
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)100.0%
Missing9
Missing (%)37.5%
Infinite0
Infinite (%)0.0%
Mean143.10667
Minimum5
Maximum317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-04-21T21:04:20.420807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile22.5
Q174.45
median124
Q3206.65
95-th percentile311.89
Maximum317
Range312
Interquartile range (IQR)132.2

Descriptive statistics

Standard deviation95.106673
Coefficient of variation (CV)0.6645859
Kurtosis-0.52224681
Mean143.10667
Median Absolute Deviation (MAD)72.4
Skewness0.50012377
Sum2146.6
Variance9045.2792
MonotonicityNot monotonic
2024-04-21T21:04:20.774531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
90.0 1
 
4.2%
102.0 1
 
4.2%
175.0 1
 
4.2%
222.3 1
 
4.2%
51.6 1
 
4.2%
30.0 1
 
4.2%
317.0 1
 
4.2%
206.0 1
 
4.2%
136.8 1
 
4.2%
309.7 1
 
4.2%
Other values (5) 5
20.8%
(Missing) 9
37.5%
ValueCountFrequency (%)
5.0 1
4.2%
30.0 1
4.2%
51.6 1
4.2%
58.9 1
4.2%
90.0 1
4.2%
102.0 1
4.2%
111.0 1
4.2%
124.0 1
4.2%
136.8 1
4.2%
175.0 1
4.2%
ValueCountFrequency (%)
317.0 1
4.2%
309.7 1
4.2%
222.3 1
4.2%
207.3 1
4.2%
206.0 1
4.2%
175.0 1
4.2%
136.8 1
4.2%
124.0 1
4.2%
111.0 1
4.2%
102.0 1
4.2%

교육 연구 및 사업화 시설구역
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)100.0%
Missing11
Missing (%)45.8%
Infinite0
Infinite (%)0.0%
Mean2730.3846
Minimum58
Maximum13007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-04-21T21:04:21.115636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile387.34
Q1753.2
median1827
Q32483
95-th percentile8476.1
Maximum13007
Range12949
Interquartile range (IQR)1729.8

Descriptive statistics

Standard deviation3388.7973
Coefficient of variation (CV)1.2411428
Kurtosis7.9103693
Mean2730.3846
Median Absolute Deviation (MAD)1073.8
Skewness2.6672385
Sum35495
Variance11483947
MonotonicityNot monotonic
2024-04-21T21:04:21.478068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
13007.0 1
 
4.2%
3059.0 1
 
4.2%
1108.5 1
 
4.2%
58.0 1
 
4.2%
2483.0 1
 
4.2%
1827.0 1
 
4.2%
753.2 1
 
4.2%
2325.6 1
 
4.2%
1749.4 1
 
4.2%
2423.4 1
 
4.2%
Other values (3) 3
 
12.5%
(Missing) 11
45.8%
ValueCountFrequency (%)
58.0 1
4.2%
606.9 1
4.2%
638.5 1
4.2%
753.2 1
4.2%
1108.5 1
4.2%
1749.4 1
4.2%
1827.0 1
4.2%
2325.6 1
4.2%
2423.4 1
4.2%
2483.0 1
4.2%
ValueCountFrequency (%)
13007.0 1
4.2%
5455.5 1
4.2%
3059.0 1
4.2%
2483.0 1
4.2%
2423.4 1
4.2%
2325.6 1
4.2%
1827.0 1
4.2%
1749.4 1
4.2%
1108.5 1
4.2%
753.2 1
4.2%

산업공공 시설구역 및 산업복합구역
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)100.0%
Missing14
Missing (%)58.3%
Infinite0
Infinite (%)0.0%
Mean88
Minimum5.6
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-04-21T21:04:21.803925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.6
5-th percentile5.735
Q127.05
median57.15
Q3110.675
95-th percentile261.365
Maximum365
Range359.4
Interquartile range (IQR)83.625

Descriptive statistics

Standard deviation107.09167
Coefficient of variation (CV)1.2169508
Kurtosis5.6407603
Mean88
Median Absolute Deviation (MAD)41.7
Skewness2.2355614
Sum880
Variance11468.627
MonotonicityNot monotonic
2024-04-21T21:04:22.152882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
365.0 1
 
4.2%
5.9 1
 
4.2%
33.2 1
 
4.2%
134.7 1
 
4.2%
123.2 1
 
4.2%
72.0 1
 
4.2%
5.6 1
 
4.2%
42.3 1
 
4.2%
25.0 1
 
4.2%
73.1 1
 
4.2%
(Missing) 14
58.3%
ValueCountFrequency (%)
5.6 1
4.2%
5.9 1
4.2%
25.0 1
4.2%
33.2 1
4.2%
42.3 1
4.2%
72.0 1
4.2%
73.1 1
4.2%
123.2 1
4.2%
134.7 1
4.2%
365.0 1
4.2%
ValueCountFrequency (%)
365.0 1
4.2%
134.7 1
4.2%
123.2 1
4.2%
73.1 1
4.2%
72.0 1
4.2%
42.3 1
4.2%
33.2 1
4.2%
25.0 1
4.2%
5.9 1
4.2%
5.6 1
4.2%

Interactions

2024-04-21T21:04:12.589690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:02.166524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:03.853453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:05.744423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:07.440122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:09.152152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:10.858330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:12.833870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:02.401598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:04.088210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:05.981831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:07.678043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:09.391308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:11.101916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:13.082332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:02.636093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:04.324998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:06.217370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:07.920289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:09.629531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:11.344013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:13.330419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:02.875100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:04.559655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:06.453746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:08.163275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:09.868538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:11.587633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:13.576933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:03.110010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:04.804936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:06.698973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:08.407560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:10.111799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:11.829106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:13.824715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:03.355706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:05.040409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:06.942048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:08.653562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:10.358165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:12.081309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:14.078398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:03.599090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:05.494658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:07.187958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:08.897369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:10.608202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T21:04:12.329182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T21:04:22.408126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지구주거구역상업구역녹지구역산업육성구역산업지원구역교육 연구 및 사업화 시설구역산업공공 시설구역 및 산업복합구역
지구1.0001.0001.0001.0001.0001.0001.0001.000
주거구역1.0001.0000.8290.7700.0000.0000.7930.489
상업구역1.0000.8291.0000.7820.3250.0000.7890.234
녹지구역1.0000.7700.7821.0000.6010.7390.6600.829
산업육성구역1.0000.0000.3250.6011.0000.3240.0000.688
산업지원구역1.0000.0000.0000.7390.3241.0000.4510.406
교육 연구 및 사업화 시설구역1.0000.7930.7890.6600.0000.4511.0000.749
산업공공 시설구역 및 산업복합구역1.0000.4890.2340.8290.6880.4060.7491.000
2024-04-21T21:04:22.710510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주거구역상업구역녹지구역산업육성구역산업지원구역교육 연구 및 사업화 시설구역산업공공 시설구역 및 산업복합구역
주거구역1.0000.6850.682-0.0600.0050.5830.000
상업구역0.6851.0000.382-0.007-0.1030.5710.143
녹지구역0.6820.3821.000-0.0660.3040.6540.030
산업육성구역-0.060-0.007-0.0661.0000.511-0.1390.042
산업지원구역0.005-0.1030.3040.5111.0000.050-0.164
교육 연구 및 사업화 시설구역0.5830.5710.654-0.1390.0501.0000.321
산업공공 시설구역 및 산업복합구역0.0000.1430.0300.042-0.1640.3211.000

Missing values

2024-04-21T21:04:14.433018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T21:04:14.853218image/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.
2024-04-21T21:04:15.198075image/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

지구주거구역상업구역녹지구역산업육성구역산업지원구역교육 연구 및 사업화 시설구역산업공공 시설구역 및 산업복합구역
0대덕 제Ⅰ지구2677.0547.010436.0660.090.013007.0365.0
1대덕 제Ⅱ지구1211.0373.0708.01876.0102.0<NA><NA>
2대덕 제Ⅲ지구16.0<NA>96.02908.0175.0<NA><NA>
3대덕 제Ⅳ지구<NA><NA>10478.0<NA><NA><NA><NA>
4대덕 제Ⅴ지구<NA><NA>901.0<NA><NA>3059.0<NA>
5광주 첨단1<NA><NA>168.62022.7222.31108.55.9
6광주 첨단2<NA><NA>191.51000.951.6<NA><NA>
7광주 첨단3<NA><NA>3628.6<NA><NA><NA><NA>
8광주 나노77.034.073.8596.030.058.033.2
9광주 신룡170.4<NA>3276.6<NA><NA><NA><NA>
지구주거구역상업구역녹지구역산업육성구역산업지원구역교육 연구 및 사업화 시설구역산업공공 시설구역 및 산업복합구역
14대구 의료R&D10.9143.2464.1260.0136.8<NA>72.0
15대구 지식서비스R&D I II<NA>8.2778.7110.5<NA>2325.6<NA>
16대구 테크노폴리스1327.5135.02863.71521.1309.71749.45.6
17부산 R&D융합7.1<NA>72.0697.05.02423.4<NA>
18부산 사업화촉진43.020.0799.93089.5207.3<NA>42.3
19부산 생산거점<NA>74.014.01974.0<NA><NA><NA>
20부산 첨단복합361.0<NA>4274.0<NA><NA><NA><NA>
21전북 사업화촉진지구1767.3209.01947.2524.0111.05455.525.0
22전북 융복합소재부품 거점지구329.574.0385.52811.4124.0638.573.1
23전북 농생명융합지구213.139.9145.1487.158.9606.9<NA>