Overview

Dataset statistics

Number of variables17
Number of observations64
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.6 KiB
Average record size in memory154.1 B

Variable types

Text1
Numeric16

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 상업용부동산 임대동향조사 중 오피스의 분기별 지역별 공실률 데이터입니다. - (단위: %) - 공표시기 : 계간(분기)
Author한국부동산원
URLhttps://www.data.go.kr/data/15069730/fileData.do

Alerts

2013_1분기 is highly overall correlated with 2013_2분기 and 14 other fieldsHigh correlation
2013_2분기 is highly overall correlated with 2013_1분기 and 14 other fieldsHigh correlation
2013_3분기 is highly overall correlated with 2013_1분기 and 13 other fieldsHigh correlation
2013_4분기 is highly overall correlated with 2013_1분기 and 14 other fieldsHigh correlation
2014_1분기 is highly overall correlated with 2013_1분기 and 11 other fieldsHigh correlation
2014_2분기 is highly overall correlated with 2013_1분기 and 14 other fieldsHigh correlation
2014_3분기 is highly overall correlated with 2013_1분기 and 14 other fieldsHigh correlation
2014_4분기 is highly overall correlated with 2013_1분기 and 14 other fieldsHigh correlation
2015_1분기 is highly overall correlated with 2013_1분기 and 14 other fieldsHigh correlation
2015_2분기 is highly overall correlated with 2013_1분기 and 14 other fieldsHigh correlation
2015_3분기 is highly overall correlated with 2013_1분기 and 13 other fieldsHigh correlation
2015_4분기 is highly overall correlated with 2013_1분기 and 14 other fieldsHigh correlation
2016_1분기 is highly overall correlated with 2013_1분기 and 14 other fieldsHigh correlation
2016_2분기 is highly overall correlated with 2013_1분기 and 13 other fieldsHigh correlation
2016_3분기 is highly overall correlated with 2013_1분기 and 12 other fieldsHigh correlation
2016_4분기 is highly overall correlated with 2013_1분기 and 14 other fieldsHigh correlation
지역 has unique valuesUnique

Reproduction

Analysis started2023-12-13 00:02:02.057290
Analysis finished2023-12-13 00:02:22.559987
Duration20.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-12-13T09:02:22.681204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length6.546875
Min length3

Characters and Unicode

Total characters419
Distinct characters87
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)100.0%

Sample

1st row서울
2nd row서울 도심
3rd row서울 도심 광화문
4th row서울 도심 동대문
5th row서울 도심 명동
ValueCountFrequency (%)
서울 27
20.1%
기타 9
 
6.7%
도심 7
 
5.2%
강남 6
 
4.5%
부산 6
 
4.5%
경기 5
 
3.7%
대전 4
 
3.0%
대구 4
 
3.0%
여의도마포 4
 
3.0%
인천 4
 
3.0%
Other values (54) 58
43.3%
2023-12-13T09:02:22.947561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
90
21.5%
31
 
7.4%
31
 
7.4%
16
 
3.8%
14
 
3.3%
14
 
3.3%
14
 
3.3%
11
 
2.6%
9
 
2.1%
9
 
2.1%
Other values (77) 180
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 328
78.3%
Space Separator 90
 
21.5%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
31
 
9.5%
31
 
9.5%
16
 
4.9%
14
 
4.3%
14
 
4.3%
14
 
4.3%
11
 
3.4%
9
 
2.7%
9
 
2.7%
9
 
2.7%
Other values (75) 170
51.8%
Space Separator
ValueCountFrequency (%)
90
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 328
78.3%
Common 91
 
21.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
31
 
9.5%
31
 
9.5%
16
 
4.9%
14
 
4.3%
14
 
4.3%
14
 
4.3%
11
 
3.4%
9
 
2.7%
9
 
2.7%
9
 
2.7%
Other values (75) 170
51.8%
Common
ValueCountFrequency (%)
90
98.9%
/ 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 328
78.3%
ASCII 91
 
21.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
90
98.9%
/ 1
 
1.1%
Hangul
ValueCountFrequency (%)
31
 
9.5%
31
 
9.5%
16
 
4.9%
14
 
4.3%
14
 
4.3%
14
 
4.3%
11
 
3.4%
9
 
2.7%
9
 
2.7%
9
 
2.7%
Other values (75) 170
51.8%

2013_1분기
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0375
Minimum0.4
Maximum25.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:23.056163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile1.905
Q15.4
median7.1
Q312.45
95-th percentile18.9
Maximum25.6
Range25.2
Interquartile range (IQR)7.05

Descriptive statistics

Standard deviation5.5172888
Coefficient of variation (CV)0.61048839
Kurtosis0.48230603
Mean9.0375
Median Absolute Deviation (MAD)3.05
Skewness0.89776682
Sum578.4
Variance30.440476
MonotonicityNot monotonic
2023-12-13T09:02:23.164763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.2 3
 
4.7%
5.7 2
 
3.1%
18.9 2
 
3.1%
13.2 2
 
3.1%
4.6 2
 
3.1%
5.4 2
 
3.1%
10.5 2
 
3.1%
6.6 2
 
3.1%
10.2 2
 
3.1%
5.9 2
 
3.1%
Other values (41) 43
67.2%
ValueCountFrequency (%)
0.4 1
1.6%
0.9 1
1.6%
1.4 1
1.6%
1.8 1
1.6%
2.5 1
1.6%
2.7 1
1.6%
2.8 1
1.6%
3.0 1
1.6%
3.9 1
1.6%
4.2 1
1.6%
ValueCountFrequency (%)
25.6 1
1.6%
22.6 1
1.6%
20.7 1
1.6%
18.9 2
3.1%
18.4 1
1.6%
16.6 1
1.6%
16.5 1
1.6%
15.1 1
1.6%
15.0 1
1.6%
14.7 1
1.6%

2013_2분기
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.678125
Minimum0.4
Maximum35.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:23.270365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile3.015
Q15.9
median7.65
Q312.5
95-th percentile19.425
Maximum35.4
Range35
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation6.0277261
Coefficient of variation (CV)0.62281962
Kurtosis3.9869194
Mean9.678125
Median Absolute Deviation (MAD)2.7
Skewness1.5131931
Sum619.4
Variance36.333482
MonotonicityNot monotonic
2023-12-13T09:02:23.379999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.6 3
 
4.7%
7.2 2
 
3.1%
8.9 2
 
3.1%
6.8 2
 
3.1%
6.6 2
 
3.1%
19.0 2
 
3.1%
5.6 2
 
3.1%
5.9 2
 
3.1%
7.0 1
 
1.6%
20.6 1
 
1.6%
Other values (45) 45
70.3%
ValueCountFrequency (%)
0.4 1
1.6%
0.8 1
1.6%
1.5 1
1.6%
3.0 1
1.6%
3.1 1
1.6%
3.5 1
1.6%
3.6 1
1.6%
3.8 1
1.6%
4.4 1
1.6%
4.6 1
1.6%
ValueCountFrequency (%)
35.4 1
1.6%
21.0 1
1.6%
20.6 1
1.6%
19.5 1
1.6%
19.0 2
3.1%
17.5 1
1.6%
17.4 1
1.6%
17.3 1
1.6%
16.2 1
1.6%
15.4 1
1.6%

2013_3분기
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.178125
Minimum0.8
Maximum24.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:23.517968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile3.015
Q16.2
median8.7
Q314.15
95-th percentile21.275
Maximum24.1
Range23.3
Interquartile range (IQR)7.95

Descriptive statistics

Standard deviation5.5107822
Coefficient of variation (CV)0.54143393
Kurtosis-0.15350551
Mean10.178125
Median Absolute Deviation (MAD)3.15
Skewness0.68077443
Sum651.4
Variance30.36872
MonotonicityNot monotonic
2023-12-13T09:02:23.649054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 3
 
4.7%
6.8 2
 
3.1%
5.6 2
 
3.1%
15.9 2
 
3.1%
11.5 2
 
3.1%
7.9 2
 
3.1%
6.2 2
 
3.1%
7.2 2
 
3.1%
8.7 2
 
3.1%
8.5 2
 
3.1%
Other values (43) 43
67.2%
ValueCountFrequency (%)
0.8 1
1.6%
2.0 1
1.6%
2.1 1
1.6%
3.0 1
1.6%
3.1 1
1.6%
3.4 1
1.6%
4.1 1
1.6%
4.4 1
1.6%
4.9 1
1.6%
5.1 1
1.6%
ValueCountFrequency (%)
24.1 1
1.6%
22.8 1
1.6%
22.0 1
1.6%
21.5 1
1.6%
20.0 1
1.6%
19.0 1
1.6%
17.6 1
1.6%
17.3 1
1.6%
16.3 1
1.6%
15.9 2
3.1%

2013_4분기
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.360938
Minimum0.1
Maximum27.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:23.777454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile4.275
Q17.675
median10.2
Q314.75
95-th percentile20.095
Maximum27.3
Range27.2
Interquartile range (IQR)7.075

Descriptive statistics

Standard deviation5.5078765
Coefficient of variation (CV)0.48480828
Kurtosis0.54026846
Mean11.360938
Median Absolute Deviation (MAD)3.15
Skewness0.61694176
Sum727.1
Variance30.336704
MonotonicityNot monotonic
2023-12-13T09:02:23.891872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.4 3
 
4.7%
9.7 2
 
3.1%
8.5 2
 
3.1%
7.1 2
 
3.1%
10.4 2
 
3.1%
7.9 2
 
3.1%
9.5 2
 
3.1%
16.3 1
 
1.6%
10.0 1
 
1.6%
12.0 1
 
1.6%
Other values (46) 46
71.9%
ValueCountFrequency (%)
0.1 1
1.6%
0.4 1
1.6%
2.6 1
1.6%
4.2 1
1.6%
4.7 1
1.6%
5.1 1
1.6%
5.4 1
1.6%
6.4 1
1.6%
6.5 1
1.6%
6.8 1
1.6%
ValueCountFrequency (%)
27.3 1
1.6%
25.6 1
1.6%
22.6 1
1.6%
20.2 1
1.6%
19.5 1
1.6%
19.1 1
1.6%
18.5 1
1.6%
18.1 1
1.6%
17.5 1
1.6%
17.2 1
1.6%

2014_1분기
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.6875
Minimum0.4
Maximum22.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:24.004900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile4.63
Q18.075
median10.65
Q315.475
95-th percentile20.935
Maximum22.5
Range22.1
Interquartile range (IQR)7.4

Descriptive statistics

Standard deviation5.1867811
Coefficient of variation (CV)0.44378876
Kurtosis-0.56024892
Mean11.6875
Median Absolute Deviation (MAD)3.1
Skewness0.27852314
Sum748
Variance26.902698
MonotonicityNot monotonic
2023-12-13T09:02:24.116742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.2 2
 
3.1%
10.1 2
 
3.1%
9.8 2
 
3.1%
8.9 1
 
1.6%
9.0 1
 
1.6%
12.5 1
 
1.6%
12.9 1
 
1.6%
5.5 1
 
1.6%
17.5 1
 
1.6%
14.4 1
 
1.6%
Other values (51) 51
79.7%
ValueCountFrequency (%)
0.4 1
1.6%
2.5 1
1.6%
3.2 1
1.6%
4.6 1
1.6%
4.8 1
1.6%
4.9 1
1.6%
5.5 1
1.6%
6.2 1
1.6%
6.3 1
1.6%
6.5 1
1.6%
ValueCountFrequency (%)
22.5 1
1.6%
22.2 1
1.6%
21.5 1
1.6%
21.1 1
1.6%
20.0 1
1.6%
19.4 1
1.6%
19.2 1
1.6%
18.5 1
1.6%
18.3 1
1.6%
18.1 1
1.6%

2014_2분기
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.132812
Minimum0.8
Maximum25.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:24.235381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile4.845
Q18.05
median11.3
Q315.725
95-th percentile21.59
Maximum25.4
Range24.6
Interquartile range (IQR)7.675

Descriptive statistics

Standard deviation5.4023233
Coefficient of variation (CV)0.44526554
Kurtosis-0.16567842
Mean12.132812
Median Absolute Deviation (MAD)3.7
Skewness0.50463808
Sum776.5
Variance29.185097
MonotonicityNot monotonic
2023-12-13T09:02:24.356699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.6 3
 
4.7%
8.7 3
 
4.7%
10.6 3
 
4.7%
11.3 2
 
3.1%
14.3 2
 
3.1%
10.1 2
 
3.1%
11.8 2
 
3.1%
10.4 1
 
1.6%
22.7 1
 
1.6%
12.0 1
 
1.6%
Other values (44) 44
68.8%
ValueCountFrequency (%)
0.8 1
1.6%
3.8 1
1.6%
3.9 1
1.6%
4.8 1
1.6%
5.1 1
1.6%
5.2 1
1.6%
6.1 1
1.6%
6.6 1
1.6%
7.1 1
1.6%
7.2 1
1.6%
ValueCountFrequency (%)
25.4 1
1.6%
25.3 1
1.6%
22.7 1
1.6%
21.8 1
1.6%
20.4 1
1.6%
20.3 1
1.6%
20.2 1
1.6%
18.8 1
1.6%
18.3 1
1.6%
18.2 1
1.6%

2014_3분기
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.701562
Minimum0.8
Maximum39.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:24.775873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile5.015
Q18.5
median11.1
Q316.375
95-th percentile22.445
Maximum39.7
Range38.9
Interquartile range (IQR)7.875

Descriptive statistics

Standard deviation6.7779402
Coefficient of variation (CV)0.53363043
Kurtosis3.1156394
Mean12.701562
Median Absolute Deviation (MAD)4.5
Skewness1.3039407
Sum812.9
Variance45.940474
MonotonicityNot monotonic
2023-12-13T09:02:24.896550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.6 2
 
3.1%
6.1 2
 
3.1%
8.5 2
 
3.1%
14.0 2
 
3.1%
11.1 2
 
3.1%
9.2 2
 
3.1%
8.7 2
 
3.1%
6.0 2
 
3.1%
17.9 1
 
1.6%
13.3 1
 
1.6%
Other values (46) 46
71.9%
ValueCountFrequency (%)
0.8 1
1.6%
2.2 1
1.6%
3.9 1
1.6%
5.0 1
1.6%
5.1 1
1.6%
5.5 1
1.6%
6.0 2
3.1%
6.1 2
3.1%
6.3 1
1.6%
6.6 1
1.6%
ValueCountFrequency (%)
39.7 1
1.6%
29.1 1
1.6%
28.1 1
1.6%
22.7 1
1.6%
21.0 1
1.6%
20.7 1
1.6%
20.5 1
1.6%
19.8 1
1.6%
19.6 1
1.6%
19.4 1
1.6%

2014_4분기
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.55
Minimum1.6
Maximum42.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:25.017133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile4.345
Q17.975
median10.85
Q315.85
95-th percentile23.59
Maximum42.6
Range41
Interquartile range (IQR)7.875

Descriptive statistics

Standard deviation7.189918
Coefficient of variation (CV)0.57290183
Kurtosis4.0733633
Mean12.55
Median Absolute Deviation (MAD)4.1
Skewness1.5794705
Sum803.2
Variance51.694921
MonotonicityNot monotonic
2023-12-13T09:02:25.131300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.8 2
 
3.1%
10.1 2
 
3.1%
8.0 2
 
3.1%
14.6 2
 
3.1%
11.1 2
 
3.1%
15.8 2
 
3.1%
6.3 2
 
3.1%
11.4 2
 
3.1%
9.3 2
 
3.1%
17.4 1
 
1.6%
Other values (45) 45
70.3%
ValueCountFrequency (%)
1.6 1
1.6%
2.2 1
1.6%
4.2 1
1.6%
4.3 1
1.6%
4.6 1
1.6%
4.9 1
1.6%
5.0 1
1.6%
5.2 1
1.6%
6.1 1
1.6%
6.3 2
3.1%
ValueCountFrequency (%)
42.6 1
1.6%
31.1 1
1.6%
28.2 1
1.6%
23.8 1
1.6%
22.4 1
1.6%
21.6 1
1.6%
20.9 1
1.6%
20.3 1
1.6%
20.2 1
1.6%
20.1 1
1.6%

2015_1분기
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.804687
Minimum1.5
Maximum43.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:25.247913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile4.205
Q18.075
median10.55
Q316.425
95-th percentile25.135
Maximum43.1
Range41.6
Interquartile range (IQR)8.35

Descriptive statistics

Standard deviation7.4074272
Coefficient of variation (CV)0.5784934
Kurtosis3.7135331
Mean12.804687
Median Absolute Deviation (MAD)3.9
Skewness1.5668644
Sum819.5
Variance54.869978
MonotonicityNot monotonic
2023-12-13T09:02:25.375132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.0 2
 
3.1%
8.5 2
 
3.1%
5.4 2
 
3.1%
18.4 2
 
3.1%
9.4 2
 
3.1%
18.9 2
 
3.1%
8.8 2
 
3.1%
10.0 2
 
3.1%
9.2 2
 
3.1%
20.2 1
 
1.6%
Other values (45) 45
70.3%
ValueCountFrequency (%)
1.5 1
1.6%
3.6 1
1.6%
3.7 1
1.6%
4.1 1
1.6%
4.8 1
1.6%
5.3 1
1.6%
5.4 2
3.1%
6.0 1
1.6%
6.1 1
1.6%
6.8 1
1.6%
ValueCountFrequency (%)
43.1 1
1.6%
31.1 1
1.6%
30.7 1
1.6%
25.6 1
1.6%
22.5 1
1.6%
22.4 1
1.6%
22.0 1
1.6%
20.3 1
1.6%
20.2 1
1.6%
18.9 2
3.1%

2015_2분기
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.704688
Minimum1.1
Maximum37.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:25.501101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile3.16
Q18.175
median10.75
Q316.15
95-th percentile23.65
Maximum37.2
Range36.1
Interquartile range (IQR)7.975

Descriptive statistics

Standard deviation7.0690344
Coefficient of variation (CV)0.55641151
Kurtosis1.6174974
Mean12.704688
Median Absolute Deviation (MAD)4.3
Skewness1.0709509
Sum813.1
Variance49.971248
MonotonicityNot monotonic
2023-12-13T09:02:25.632852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.2 3
 
4.7%
16.1 2
 
3.1%
8.1 2
 
3.1%
9.0 2
 
3.1%
14.2 2
 
3.1%
8.8 2
 
3.1%
16.3 2
 
3.1%
10.2 1
 
1.6%
14.8 1
 
1.6%
8.3 1
 
1.6%
Other values (46) 46
71.9%
ValueCountFrequency (%)
1.1 1
1.6%
2.3 1
1.6%
3.0 1
1.6%
3.1 1
1.6%
3.5 1
1.6%
4.1 1
1.6%
5.3 1
1.6%
5.5 1
1.6%
5.7 1
1.6%
6.6 1
1.6%
ValueCountFrequency (%)
37.2 1
1.6%
31.3 1
1.6%
28.8 1
1.6%
23.8 1
1.6%
22.8 1
1.6%
22.6 1
1.6%
21.8 1
1.6%
21.7 1
1.6%
20.8 1
1.6%
18.8 1
1.6%

2015_3분기
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.860938
Minimum1.3
Maximum38.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:25.771847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile3.165
Q17.975
median11.1
Q317.5
95-th percentile23.695
Maximum38.9
Range37.6
Interquartile range (IQR)9.525

Descriptive statistics

Standard deviation7.2811916
Coefficient of variation (CV)0.56614781
Kurtosis1.7503638
Mean12.860938
Median Absolute Deviation (MAD)4.25
Skewness1.0434248
Sum823.1
Variance53.015751
MonotonicityNot monotonic
2023-12-13T09:02:25.895569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.3 3
 
4.7%
11.0 2
 
3.1%
19.5 2
 
3.1%
17.5 2
 
3.1%
9.0 2
 
3.1%
15.1 1
 
1.6%
23.8 1
 
1.6%
31.6 1
 
1.6%
22.7 1
 
1.6%
10.1 1
 
1.6%
Other values (48) 48
75.0%
ValueCountFrequency (%)
1.3 1
1.6%
1.6 1
1.6%
1.7 1
1.6%
3.0 1
1.6%
4.1 1
1.6%
4.3 1
1.6%
5.0 1
1.6%
5.1 1
1.6%
5.5 1
1.6%
6.6 1
1.6%
ValueCountFrequency (%)
38.9 1
1.6%
31.6 1
1.6%
28.2 1
1.6%
23.8 1
1.6%
23.1 1
1.6%
22.7 1
1.6%
22.2 1
1.6%
21.8 1
1.6%
19.5 2
3.1%
19.1 1
1.6%

2015_4분기
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.44375
Minimum0.5
Maximum41.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:26.028273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile3.215
Q18.45
median11.45
Q317.35
95-th percentile24.11
Maximum41.6
Range41.1
Interquartile range (IQR)8.9

Descriptive statistics

Standard deviation7.9007509
Coefficient of variation (CV)0.58768952
Kurtosis1.9786584
Mean13.44375
Median Absolute Deviation (MAD)4.9
Skewness1.0492499
Sum860.4
Variance62.421865
MonotonicityNot monotonic
2023-12-13T09:02:26.150872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.7 2
 
3.1%
8.1 2
 
3.1%
19.6 2
 
3.1%
15.8 2
 
3.1%
8.5 2
 
3.1%
10.0 2
 
3.1%
9.1 2
 
3.1%
9.0 2
 
3.1%
0.5 1
 
1.6%
16.4 1
 
1.6%
Other values (46) 46
71.9%
ValueCountFrequency (%)
0.5 1
1.6%
1.2 1
1.6%
1.6 1
1.6%
3.2 1
1.6%
3.3 1
1.6%
4.1 1
1.6%
4.6 1
1.6%
5.0 1
1.6%
5.1 1
1.6%
5.9 1
1.6%
ValueCountFrequency (%)
41.6 1
1.6%
36.5 1
1.6%
26.8 1
1.6%
24.2 1
1.6%
23.6 1
1.6%
23.4 1
1.6%
23.0 1
1.6%
22.6 1
1.6%
22.1 1
1.6%
21.9 1
1.6%

2016_1분기
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.140625
Minimum0.5
Maximum39.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:26.266189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile4.03
Q18.825
median12.6
Q318.3
95-th percentile26.11
Maximum39.5
Range39
Interquartile range (IQR)9.475

Descriptive statistics

Standard deviation7.9977322
Coefficient of variation (CV)0.56558548
Kurtosis1.0711316
Mean14.140625
Median Absolute Deviation (MAD)4.85
Skewness0.87629937
Sum905
Variance63.96372
MonotonicityNot monotonic
2023-12-13T09:02:26.418551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.2 3
 
4.7%
10.4 2
 
3.1%
14.8 2
 
3.1%
17.8 2
 
3.1%
7.8 2
 
3.1%
4.7 2
 
3.1%
24.9 2
 
3.1%
4.2 2
 
3.1%
7.3 2
 
3.1%
10.2 1
 
1.6%
Other values (44) 44
68.8%
ValueCountFrequency (%)
0.5 1
1.6%
1.2 1
1.6%
2.6 1
1.6%
4.0 1
1.6%
4.2 2
3.1%
4.7 2
3.1%
5.3 1
1.6%
7.3 2
3.1%
7.8 2
3.1%
8.1 1
1.6%
ValueCountFrequency (%)
39.5 1
1.6%
37.2 1
1.6%
28.4 1
1.6%
26.2 1
1.6%
25.6 1
1.6%
24.9 2
3.1%
24.4 1
1.6%
23.9 1
1.6%
23.4 1
1.6%
23.1 1
1.6%

2016_2분기
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.085938
Minimum0.5
Maximum41.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:26.551896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile4.3
Q19.325
median11.95
Q318.35
95-th percentile26.635
Maximum41.3
Range40.8
Interquartile range (IQR)9.025

Descriptive statistics

Standard deviation8.137077
Coefficient of variation (CV)0.57767379
Kurtosis1.4271088
Mean14.085938
Median Absolute Deviation (MAD)5.4
Skewness1.0112674
Sum901.5
Variance66.212021
MonotonicityNot monotonic
2023-12-13T09:02:26.680264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.5 3
 
4.7%
10.4 2
 
3.1%
11.1 2
 
3.1%
4.3 2
 
3.1%
10.5 2
 
3.1%
25.7 2
 
3.1%
21.4 1
 
1.6%
25.0 1
 
1.6%
9.1 1
 
1.6%
18.5 1
 
1.6%
Other values (47) 47
73.4%
ValueCountFrequency (%)
0.5 1
1.6%
1.2 1
1.6%
3.5 1
1.6%
4.3 2
3.1%
4.7 1
1.6%
5.2 1
1.6%
5.3 1
1.6%
5.7 1
1.6%
6.2 1
1.6%
6.6 1
1.6%
ValueCountFrequency (%)
41.3 1
1.6%
37.7 1
1.6%
27.8 1
1.6%
26.8 1
1.6%
25.7 2
3.1%
25.0 1
1.6%
24.2 1
1.6%
24.0 1
1.6%
23.5 1
1.6%
22.4 1
1.6%

2016_3분기
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.779687
Minimum1.2
Maximum40.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:26.820964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile4.6
Q18.05
median11.45
Q318.45
95-th percentile27.665
Maximum40.7
Range39.5
Interquartile range (IQR)10.4

Descriptive statistics

Standard deviation8.1506681
Coefficient of variation (CV)0.59149876
Kurtosis1.3759489
Mean13.779687
Median Absolute Deviation (MAD)5.7
Skewness1.0757769
Sum881.9
Variance66.43339
MonotonicityNot monotonic
2023-12-13T09:02:26.941450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.8 4
 
6.2%
4.6 3
 
4.7%
13.3 3
 
4.7%
7.9 3
 
4.7%
17.2 3
 
4.7%
5.5 2
 
3.1%
10.1 2
 
3.1%
9.5 2
 
3.1%
23.2 1
 
1.6%
22.8 1
 
1.6%
Other values (40) 40
62.5%
ValueCountFrequency (%)
1.2 1
 
1.6%
2.2 1
 
1.6%
2.5 1
 
1.6%
4.6 3
4.7%
4.7 1
 
1.6%
5.3 1
 
1.6%
5.5 2
3.1%
5.8 1
 
1.6%
6.7 1
 
1.6%
7.0 1
 
1.6%
ValueCountFrequency (%)
40.7 1
1.6%
37.4 1
1.6%
29.3 1
1.6%
27.8 1
1.6%
26.9 1
1.6%
25.4 1
1.6%
24.5 1
1.6%
23.2 1
1.6%
23.1 1
1.6%
22.8 1
1.6%

2016_4분기
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.809375
Minimum1.2
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:02:27.067898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile4.615
Q18.475
median12.75
Q317.775
95-th percentile27.71
Maximum40
Range38.8
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation7.9314667
Coefficient of variation (CV)0.57435378
Kurtosis1.4815678
Mean13.809375
Median Absolute Deviation (MAD)4.8
Skewness1.1139635
Sum883.8
Variance62.908165
MonotonicityNot monotonic
2023-12-13T09:02:27.207803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 2
 
3.1%
8.6 2
 
3.1%
10.4 2
 
3.1%
17.1 2
 
3.1%
22.4 2
 
3.1%
9.0 2
 
3.1%
6.3 2
 
3.1%
13.5 2
 
3.1%
10.9 2
 
3.1%
13.9 2
 
3.1%
Other values (43) 44
68.8%
ValueCountFrequency (%)
1.2 1
1.6%
2.2 1
1.6%
3.8 1
1.6%
4.6 1
1.6%
4.7 1
1.6%
5.7 1
1.6%
5.9 1
1.6%
6.1 1
1.6%
6.2 1
1.6%
6.3 2
3.1%
ValueCountFrequency (%)
40.0 1
1.6%
36.9 1
1.6%
29.5 1
1.6%
27.8 1
1.6%
27.2 1
1.6%
25.4 1
1.6%
24.7 1
1.6%
23.7 1
1.6%
22.4 2
3.1%
20.7 1
1.6%

Interactions

2023-12-13T09:02:21.009131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:02.518612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:03.701177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:04.852441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:05.910184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:07.193576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:08.238028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:09.475600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:10.778769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:12.126436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:13.256416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:14.445883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:15.706000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:17.061241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:18.166725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:19.399632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:21.083717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:02.589000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:03.770137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:04.913100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:05.968222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:07.252875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:08.336403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:09.548767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:10.849443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:12.201727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:13.332737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:14.512808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:15.769995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:17.128231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:18.229356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:19.481749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:21.166983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:02.668870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:03.844145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:04.978835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:06.038251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:07.316678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:08.412251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:09.628894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:10.917558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:12.284044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:13.412375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:14.589522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-13T09:02:05.843334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:07.128637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:08.165762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:09.402108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:10.692326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:12.041835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:13.188679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:14.366372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:15.628246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:16.993515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:18.091718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:19.315090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:20.901852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:02:27.313524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역2013_1분기2013_2분기2013_3분기2013_4분기2014_1분기2014_2분기2014_3분기2014_4분기2015_1분기2015_2분기2015_3분기2015_4분기2016_1분기2016_2분기2016_3분기2016_4분기
지역1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2013_1분기1.0001.0000.7940.9030.8780.8710.8050.7310.7460.7330.8470.7180.6760.6860.6100.6420.691
2013_2분기1.0000.7941.0000.8270.7780.7460.7240.7740.7750.7180.7350.7710.7570.6330.6280.6830.615
2013_3분기1.0000.9030.8271.0000.9260.9070.8780.7090.7680.7010.8370.7640.7470.7110.6330.7160.672
2013_4분기1.0000.8780.7780.9261.0000.9310.9100.7950.8220.7490.8440.6930.6710.7400.5820.7590.740
2014_1분기1.0000.8710.7460.9070.9311.0000.9160.5980.7050.5840.6040.5940.5340.5090.4920.6290.559
2014_2분기1.0000.8050.7240.8780.9100.9161.0000.8390.8330.7370.8570.7180.6790.7400.7390.7530.741
2014_3분기1.0000.7310.7740.7090.7950.5980.8391.0000.8810.9090.8610.8010.8090.6760.8360.6700.646
2014_4분기1.0000.7460.7750.7680.8220.7050.8330.8811.0000.8990.8600.9410.9280.8920.7750.8840.883
2015_1분기1.0000.7330.7180.7010.7490.5840.7370.9090.8991.0000.8800.8510.8290.7260.9000.7140.744
2015_2분기1.0000.8470.7350.8370.8440.6040.8570.8610.8600.8801.0000.9460.9320.8230.8530.8230.805
2015_3분기1.0000.7180.7710.7640.6930.5940.7180.8010.9410.8510.9461.0000.9910.9270.8930.9450.933
2015_4분기1.0000.6760.7570.7470.6710.5340.6790.8090.9280.8290.9320.9911.0000.9440.8860.9310.921
2016_1분기1.0000.6860.6330.7110.7400.5090.7400.6760.8920.7260.8230.9270.9441.0000.9150.9710.970
2016_2분기1.0000.6100.6280.6330.5820.4920.7390.8360.7750.9000.8530.8930.8860.9151.0000.8950.884
2016_3분기1.0000.6420.6830.7160.7590.6290.7530.6700.8840.7140.8230.9450.9310.9710.8951.0000.979
2016_4분기1.0000.6910.6150.6720.7400.5590.7410.6460.8830.7440.8050.9330.9210.9700.8840.9791.000
2023-12-13T09:02:27.463128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2013_1분기2013_2분기2013_3분기2013_4분기2014_1분기2014_2분기2014_3분기2014_4분기2015_1분기2015_2분기2015_3분기2015_4분기2016_1분기2016_2분기2016_3분기2016_4분기
2013_1분기1.0000.8930.8530.8220.8310.7270.7070.6830.6070.6430.6220.6450.6300.6150.5990.623
2013_2분기0.8931.0000.9140.8710.8780.7450.6880.6260.5500.5640.5090.5610.5550.5340.5200.564
2013_3분기0.8530.9141.0000.9140.8880.7750.7230.6230.5930.6010.5380.5560.5370.5270.4980.552
2013_4분기0.8220.8710.9141.0000.9540.8200.7130.6220.5800.5760.5510.5920.5790.5520.5060.576
2014_1분기0.8310.8780.8880.9541.0000.8260.7110.6070.5310.5250.4990.5230.5160.4940.4680.517
2014_2분기0.7270.7450.7750.8200.8261.0000.9140.8420.7500.7120.6550.6550.6460.6420.5930.633
2014_3분기0.7070.6880.7230.7130.7110.9141.0000.9310.8050.7570.7070.7090.6900.6890.6420.682
2014_4분기0.6830.6260.6230.6220.6070.8420.9311.0000.8940.8550.7960.7960.7790.7750.7190.765
2015_1분기0.6070.5500.5930.5800.5310.7500.8050.8941.0000.9500.8810.8540.8340.8320.7570.788
2015_2분기0.6430.5640.6010.5760.5250.7120.7570.8550.9501.0000.9230.8880.8520.8490.7910.813
2015_3분기0.6220.5090.5380.5510.4990.6550.7070.7960.8810.9231.0000.9630.9240.9260.8960.893
2015_4분기0.6450.5610.5560.5920.5230.6550.7090.7960.8540.8880.9631.0000.9510.9420.9060.921
2016_1분기0.6300.5550.5370.5790.5160.6460.6900.7790.8340.8520.9240.9511.0000.9810.9420.943
2016_2분기0.6150.5340.5270.5520.4940.6420.6890.7750.8320.8490.9260.9420.9811.0000.9640.963
2016_3분기0.5990.5200.4980.5060.4680.5930.6420.7190.7570.7910.8960.9060.9420.9641.0000.961
2016_4분기0.6230.5640.5520.5760.5170.6330.6820.7650.7880.8130.8930.9210.9430.9630.9611.000

Missing values

2023-12-13T09:02:22.337175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:02:22.500170image/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

지역2013_1분기2013_2분기2013_3분기2013_4분기2014_1분기2014_2분기2014_3분기2014_4분기2015_1분기2015_2분기2015_3분기2015_4분기2016_1분기2016_2분기2016_3분기2016_4분기
0서울6.26.46.88.48.910.410.410.812.010.210.110.210.110.29.49.4
1서울 도심6.26.87.17.78.212.212.614.113.411.19.810.710.911.510.310.4
2서울 도심 광화문4.64.95.17.07.87.16.04.34.85.75.58.28.612.111.611.2
3서울 도심 동대문5.24.64.95.46.27.48.79.35.47.67.96.28.99.45.86.6
4서울 도심 명동5.96.66.86.87.47.612.911.410.59.210.514.615.514.113.313.0
5서울 도심 서울역1.41.52.12.62.521.821.031.130.720.817.515.414.114.611.312.9
6서울 도심 종로10.19.910.010.511.010.810.810.19.48.16.66.67.37.07.96.5
7서울 도심 충무로12.019.019.018.518.518.819.620.318.918.313.417.216.014.510.113.9
8서울 강남5.96.26.99.710.110.110.310.110.611.011.511.510.410.09.08.6
9서울 강남 강남대로8.810.18.512.212.813.011.111.314.314.218.522.624.922.418.613.9
지역2013_1분기2013_2분기2013_3분기2013_4분기2014_1분기2014_2분기2014_3분기2014_4분기2015_1분기2015_2분기2015_3분기2015_4분기2016_1분기2016_2분기2016_3분기2016_4분기
54경기 일산동구7.27.26.56.56.55.26.06.57.57.57.58.58.56.67.07.0
55경기 평촌범계1.87.75.27.19.88.16.16.15.43.51.61.64.24.39.56.3
56강원13.214.717.319.119.218.318.119.418.516.117.817.519.221.417.718.0
57충북10.710.315.616.413.717.820.520.220.321.721.823.023.425.726.927.2
58충남8.48.97.713.011.811.610.29.08.59.99.410.010.210.310.110.1
59전북16.617.516.317.519.420.320.720.922.522.623.124.225.625.023.122.4
60전남14.714.914.113.414.314.915.015.418.618.618.319.318.918.718.218.2
61경북12.610.410.811.410.811.412.012.614.914.213.715.817.818.217.218.8
62경남6.57.17.27.57.57.68.58.09.29.09.09.19.710.412.613.5
63제주3.93.54.16.48.17.97.97.97.98.811.910.910.510.510.810.4