Overview

Dataset statistics

Number of variables15
Number of observations271
Missing cells796
Missing cells (%)19.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.3 KiB
Average record size in memory133.5 B

Variable types

Text2
Unsupported2
Numeric11

Dataset

Description2006년 ~ 2017년 기간 동안 법정감염병 중 3군에 속하는 설치류매개 감염병인 신증후군출혈열의 지역별/연도별 발생현황. (단위: 건)
Author질병관리청
URLhttps://www.data.go.kr/data/15033738/fileData.do

Alerts

2006 is highly overall correlated with 2007High correlation
2007 is highly overall correlated with 2006 and 4 other fieldsHigh correlation
2008 is highly overall correlated with 2007 and 1 other fieldsHigh correlation
2009 is highly overall correlated with 2007 and 1 other fieldsHigh correlation
2010 is highly overall correlated with 2007High correlation
2011 is highly overall correlated with 2012 and 1 other fieldsHigh correlation
2012 is highly overall correlated with 2011 and 1 other fieldsHigh correlation
2013 is highly overall correlated with 2007 and 4 other fieldsHigh correlation
2014 is highly overall correlated with 2013 and 2 other fieldsHigh correlation
2015 is highly overall correlated with 2014 and 1 other fieldsHigh correlation
2016 is highly overall correlated with 2013 and 2 other fieldsHigh correlation
연도 시도 has 253 (93.4%) missing valuesMissing
Unnamed: 1 has 271 (100.0%) missing valuesMissing
Unnamed: 3 has 271 (100.0%) missing valuesMissing
Unnamed: 1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
2006 has 113 (41.7%) zerosZeros
2007 has 93 (34.3%) zerosZeros
2008 has 111 (41.0%) zerosZeros
2009 has 126 (46.5%) zerosZeros
2010 has 83 (30.6%) zerosZeros
2011 has 114 (42.1%) zerosZeros
2012 has 116 (42.8%) zerosZeros
2013 has 97 (35.8%) zerosZeros
2014 has 114 (42.1%) zerosZeros
2015 has 111 (41.0%) zerosZeros
2016 has 78 (28.8%) zerosZeros

Reproduction

Analysis started2023-12-12 09:42:14.067085
Analysis finished2023-12-12 09:42:28.862087
Duration14.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도 시도
Text

MISSING 

Distinct18
Distinct (%)100.0%
Missing253
Missing (%)93.4%
Memory size2.2 KiB
2023-12-12T18:42:29.027489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters36
Distinct characters22
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

Unique18 ?
Unique (%)100.0%

Sample

1st row전국
2nd row서울
3rd row부산
4th row대구
5th row인천
ValueCountFrequency (%)
경기 1
 
5.6%
부산 1
 
5.6%
강원 1
 
5.6%
제주 1
 
5.6%
경남 1
 
5.6%
경북 1
 
5.6%
전남 1
 
5.6%
전북 1
 
5.6%
충남 1
 
5.6%
전국 1
 
5.6%
Other values (8) 8
44.4%
2023-12-12T18:42:29.402049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
전 4
 
11.1%
경 3
 
8.3%
남 3
 
8.3%
북 3
 
8.3%
주 2
 
5.6%
충 2
 
5.6%
울 2
 
5.6%
대 2
 
5.6%
산 2
 
5.6%
천 1
 
2.8%
Other values (12) 12
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 36
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 4
 
11.1%
경 3
 
8.3%
남 3
 
8.3%
북 3
 
8.3%
주 2
 
5.6%
충 2
 
5.6%
울 2
 
5.6%
대 2
 
5.6%
산 2
 
5.6%
천 1
 
2.8%
Other values (12) 12
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 36
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 4
 
11.1%
경 3
 
8.3%
남 3
 
8.3%
북 3
 
8.3%
주 2
 
5.6%
충 2
 
5.6%
울 2
 
5.6%
대 2
 
5.6%
산 2
 
5.6%
천 1
 
2.8%
Other values (12) 12
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 36
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
전 4
 
11.1%
경 3
 
8.3%
남 3
 
8.3%
북 3
 
8.3%
주 2
 
5.6%
충 2
 
5.6%
울 2
 
5.6%
대 2
 
5.6%
산 2
 
5.6%
천 1
 
2.8%
Other values (12) 12
33.3%

Unnamed: 1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing271
Missing (%)100.0%
Memory size2.5 KiB

Unnamed: 2
Text

Distinct247
Distinct (%)91.5%
Missing1
Missing (%)0.4%
Memory size2.2 KiB
2023-12-12T18:42:29.801309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.362963
Min length2

Characters and Unicode

Total characters908
Distinct characters141
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique240 ?
Unique (%)88.9%

Sample

1st row서울
2nd row강남구
3rd row강동구
4th row강북구
5th row강서구
ValueCountFrequency (%)
동구 6
 
2.0%
남구 6
 
2.0%
중구 6
 
2.0%
서구 5
 
1.7%
북구 5
 
1.7%
창원시 5
 
1.7%
청주시 4
 
1.3%
수원시 4
 
1.3%
성남시 3
 
1.0%
포항시 3
 
1.0%
Other values (244) 255
84.4%
2023-12-12T18:42:30.370759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 107
 
11.8%
시 102
 
11.2%
군 86
 
9.5%
32
 
3.5%
주 26
 
2.9%
산 25
 
2.8%
천 25
 
2.8%
양 22
 
2.4%
성 21
 
2.3%
동 20
 
2.2%
Other values (131) 442
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 876
96.5%
Space Separator 32
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 107
 
12.2%
시 102
 
11.6%
군 86
 
9.8%
주 26
 
3.0%
산 25
 
2.9%
천 25
 
2.9%
양 22
 
2.5%
성 21
 
2.4%
동 20
 
2.3%
남 20
 
2.3%
Other values (130) 422
48.2%
Space Separator
ValueCountFrequency (%)
32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 876
96.5%
Common 32
 
3.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 107
 
12.2%
시 102
 
11.6%
군 86
 
9.8%
주 26
 
3.0%
산 25
 
2.9%
천 25
 
2.9%
양 22
 
2.5%
성 21
 
2.4%
동 20
 
2.3%
남 20
 
2.3%
Other values (130) 422
48.2%
Common
ValueCountFrequency (%)
32
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 876
96.5%
ASCII 32
 
3.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 107
 
12.2%
시 102
 
11.6%
군 86
 
9.8%
주 26
 
3.0%
산 25
 
2.9%
천 25
 
2.9%
양 22
 
2.5%
성 21
 
2.4%
동 20
 
2.3%
남 20
 
2.3%
Other values (130) 422
48.2%
ASCII
ValueCountFrequency (%)
32
100.0%

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing271
Missing (%)100.0%
Memory size2.5 KiB

2006
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5830258
Minimum0
Maximum422
Zeros113
Zeros (%)41.7%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T18:42:30.531111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile14
Maximum422
Range422
Interquartile range (IQR)3

Descriptive statistics

Standard deviation26.769239
Coefficient of variation (CV)5.8409531
Kurtosis221.20119
Mean4.5830258
Median Absolute Deviation (MAD)1
Skewness14.302407
Sum1242
Variance716.59216
MonotonicityNot monotonic
2023-12-12T18:42:30.672205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 113
41.7%
1 62
22.9%
2 27
 
10.0%
3 17
 
6.3%
4 16
 
5.9%
5 7
 
2.6%
7 4
 
1.5%
6 4
 
1.5%
15 2
 
0.7%
8 2
 
0.7%
Other values (15) 17
 
6.3%
ValueCountFrequency (%)
0 113
41.7%
1 62
22.9%
2 27
 
10.0%
3 17
 
6.3%
4 16
 
5.9%
5 7
 
2.6%
6 4
 
1.5%
7 4
 
1.5%
8 2
 
0.7%
9 2
 
0.7%
ValueCountFrequency (%)
422 1
0.4%
76 1
0.4%
60 1
0.4%
58 1
0.4%
42 1
0.4%
37 1
0.4%
28 1
0.4%
27 1
0.4%
24 1
0.4%
20 1
0.4%

2007
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9298893
Minimum0
Maximum450
Zeros93
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T18:42:30.830099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile14
Maximum450
Range450
Interquartile range (IQR)3

Descriptive statistics

Standard deviation28.543772
Coefficient of variation (CV)5.7899418
Kurtosis221.24416
Mean4.9298893
Median Absolute Deviation (MAD)1
Skewness14.315963
Sum1336
Variance814.74692
MonotonicityNot monotonic
2023-12-12T18:42:30.987408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 93
34.3%
1 65
24.0%
2 38
14.0%
3 22
 
8.1%
4 19
 
7.0%
5 6
 
2.2%
7 4
 
1.5%
6 3
 
1.1%
12 2
 
0.7%
20 2
 
0.7%
Other values (15) 17
 
6.3%
ValueCountFrequency (%)
0 93
34.3%
1 65
24.0%
2 38
14.0%
3 22
 
8.1%
4 19
 
7.0%
5 6
 
2.2%
6 3
 
1.1%
7 4
 
1.5%
8 1
 
0.4%
9 2
 
0.7%
ValueCountFrequency (%)
450 1
0.4%
90 1
0.4%
66 1
0.4%
48 1
0.4%
46 1
0.4%
39 1
0.4%
34 1
0.4%
27 1
0.4%
20 2
0.7%
18 1
0.4%

2008
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1070111
Minimum0
Maximum375
Zeros111
Zeros (%)41.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T18:42:31.134198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile9
Maximum375
Range375
Interquartile range (IQR)2

Descriptive statistics

Standard deviation23.816731
Coefficient of variation (CV)5.7990422
Kurtosis220.14047
Mean4.1070111
Median Absolute Deviation (MAD)1
Skewness14.268729
Sum1113
Variance567.23665
MonotonicityNot monotonic
2023-12-12T18:42:31.277860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 111
41.0%
1 56
20.7%
2 49
18.1%
4 15
 
5.5%
3 11
 
4.1%
7 6
 
2.2%
6 5
 
1.8%
9 3
 
1.1%
8 2
 
0.7%
22 2
 
0.7%
Other values (11) 11
 
4.1%
ValueCountFrequency (%)
0 111
41.0%
1 56
20.7%
2 49
18.1%
3 11
 
4.1%
4 15
 
5.5%
6 5
 
1.8%
7 6
 
2.2%
8 2
 
0.7%
9 3
 
1.1%
10 1
 
0.4%
ValueCountFrequency (%)
375 1
0.4%
76 1
0.4%
59 1
0.4%
36 1
0.4%
34 1
0.4%
29 1
0.4%
26 1
0.4%
25 1
0.4%
24 1
0.4%
22 2
0.7%

2009
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6605166
Minimum0
Maximum334
Zeros126
Zeros (%)46.5%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T18:42:31.422777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile9
Maximum334
Range334
Interquartile range (IQR)2

Descriptive statistics

Standard deviation21.21362
Coefficient of variation (CV)5.7952529
Kurtosis220.00905
Mean3.6605166
Median Absolute Deviation (MAD)1
Skewness14.251271
Sum992
Variance450.01766
MonotonicityNot monotonic
2023-12-12T18:42:31.572576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 126
46.5%
1 55
20.3%
2 27
 
10.0%
3 26
 
9.6%
5 10
 
3.7%
4 6
 
2.2%
7 4
 
1.5%
9 2
 
0.7%
334 1
 
0.4%
12 1
 
0.4%
Other values (13) 13
 
4.8%
ValueCountFrequency (%)
0 126
46.5%
1 55
20.3%
2 27
 
10.0%
3 26
 
9.6%
4 6
 
2.2%
5 10
 
3.7%
6 1
 
0.4%
7 4
 
1.5%
8 1
 
0.4%
9 2
 
0.7%
ValueCountFrequency (%)
334 1
0.4%
62 1
0.4%
47 1
0.4%
43 1
0.4%
36 1
0.4%
31 1
0.4%
27 1
0.4%
23 1
0.4%
18 1
0.4%
15 1
0.4%

2010
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1734317
Minimum0
Maximum473
Zeros83
Zeros (%)30.6%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T18:42:31.693465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile12
Maximum473
Range473
Interquartile range (IQR)3

Descriptive statistics

Standard deviation29.87882
Coefficient of variation (CV)5.7754352
Kurtosis224.90401
Mean5.1734317
Median Absolute Deviation (MAD)1
Skewness14.465376
Sum1402
Variance892.74388
MonotonicityNot monotonic
2023-12-12T18:42:31.819841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 83
30.6%
1 76
28.0%
2 31
 
11.4%
3 22
 
8.1%
4 19
 
7.0%
5 10
 
3.7%
9 4
 
1.5%
7 4
 
1.5%
6 4
 
1.5%
11 3
 
1.1%
Other values (14) 15
 
5.5%
ValueCountFrequency (%)
0 83
30.6%
1 76
28.0%
2 31
 
11.4%
3 22
 
8.1%
4 19
 
7.0%
5 10
 
3.7%
6 4
 
1.5%
7 4
 
1.5%
8 1
 
0.4%
9 4
 
1.5%
ValueCountFrequency (%)
473 1
0.4%
91 1
0.4%
51 1
0.4%
50 1
0.4%
48 1
0.4%
47 1
0.4%
36 1
0.4%
30 1
0.4%
28 1
0.4%
21 2
0.7%

2011
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0701107
Minimum0
Maximum370
Zeros114
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T18:42:31.934025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile11.5
Maximum370
Range370
Interquartile range (IQR)2

Descriptive statistics

Standard deviation23.72978
Coefficient of variation (CV)5.8302543
Kurtosis212.03367
Mean4.0701107
Median Absolute Deviation (MAD)1
Skewness13.955409
Sum1103
Variance563.10247
MonotonicityNot monotonic
2023-12-12T18:42:32.079217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 114
42.1%
1 71
26.2%
2 32
 
11.8%
3 14
 
5.2%
4 8
 
3.0%
5 4
 
1.5%
6 4
 
1.5%
7 4
 
1.5%
11 2
 
0.7%
14 2
 
0.7%
Other values (15) 16
 
5.9%
ValueCountFrequency (%)
0 114
42.1%
1 71
26.2%
2 32
 
11.8%
3 14
 
5.2%
4 8
 
3.0%
5 4
 
1.5%
6 4
 
1.5%
7 4
 
1.5%
8 1
 
0.4%
9 2
 
0.7%
ValueCountFrequency (%)
370 1
0.4%
95 1
0.4%
51 1
0.4%
43 1
0.4%
37 1
0.4%
26 1
0.4%
23 1
0.4%
22 1
0.4%
21 1
0.4%
20 1
0.4%

2012
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0221402
Minimum0
Maximum364
Zeros116
Zeros (%)42.8%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T18:42:32.203255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile11.5
Maximum364
Range364
Interquartile range (IQR)2

Descriptive statistics

Standard deviation23.441485
Coefficient of variation (CV)5.8281123
Kurtosis208.98541
Mean4.0221402
Median Absolute Deviation (MAD)1
Skewness13.864174
Sum1090
Variance549.50321
MonotonicityNot monotonic
2023-12-12T18:42:32.330460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 116
42.8%
1 60
22.1%
2 33
 
12.2%
3 19
 
7.0%
4 11
 
4.1%
5 8
 
3.0%
6 6
 
2.2%
7 2
 
0.7%
10 2
 
0.7%
13 1
 
0.4%
Other values (13) 13
 
4.8%
ValueCountFrequency (%)
0 116
42.8%
1 60
22.1%
2 33
 
12.2%
3 19
 
7.0%
4 11
 
4.1%
5 8
 
3.0%
6 6
 
2.2%
7 2
 
0.7%
10 2
 
0.7%
13 1
 
0.4%
ValueCountFrequency (%)
364 1
0.4%
106 1
0.4%
44 1
0.4%
38 1
0.4%
34 1
0.4%
26 1
0.4%
23 1
0.4%
20 1
0.4%
19 1
0.4%
18 1
0.4%

2013
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.797048
Minimum0
Maximum527
Zeros97
Zeros (%)35.8%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T18:42:32.450696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile15
Maximum527
Range527
Interquartile range (IQR)3

Descriptive statistics

Standard deviation33.840138
Coefficient of variation (CV)5.8374777
Kurtosis210.92052
Mean5.797048
Median Absolute Deviation (MAD)1
Skewness13.906498
Sum1571
Variance1145.155
MonotonicityNot monotonic
2023-12-12T18:42:32.580862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 97
35.8%
1 62
22.9%
2 34
 
12.5%
3 15
 
5.5%
4 15
 
5.5%
6 9
 
3.3%
5 8
 
3.0%
7 6
 
2.2%
8 3
 
1.1%
15 3
 
1.1%
Other values (17) 19
 
7.0%
ValueCountFrequency (%)
0 97
35.8%
1 62
22.9%
2 34
 
12.5%
3 15
 
5.5%
4 15
 
5.5%
5 8
 
3.0%
6 9
 
3.3%
7 6
 
2.2%
8 3
 
1.1%
9 1
 
0.4%
ValueCountFrequency (%)
527 1
0.4%
127 1
0.4%
93 1
0.4%
62 1
0.4%
61 1
0.4%
41 1
0.4%
27 1
0.4%
26 1
0.4%
25 1
0.4%
23 1
0.4%

2014
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7896679
Minimum0
Maximum344
Zeros114
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T18:42:32.681249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile9
Maximum344
Range344
Interquartile range (IQR)2

Descriptive statistics

Standard deviation22.043981
Coefficient of variation (CV)5.8168634
Kurtosis212.86056
Mean3.7896679
Median Absolute Deviation (MAD)1
Skewness14.006516
Sum1027
Variance485.93708
MonotonicityNot monotonic
2023-12-12T18:42:32.796276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 114
42.1%
1 67
24.7%
2 34
 
12.5%
3 15
 
5.5%
4 10
 
3.7%
6 5
 
1.8%
7 4
 
1.5%
5 4
 
1.5%
8 3
 
1.1%
9 2
 
0.7%
Other values (10) 13
 
4.8%
ValueCountFrequency (%)
0 114
42.1%
1 67
24.7%
2 34
 
12.5%
3 15
 
5.5%
4 10
 
3.7%
5 4
 
1.5%
6 5
 
1.8%
7 4
 
1.5%
8 3
 
1.1%
9 2
 
0.7%
ValueCountFrequency (%)
344 1
0.4%
92 1
0.4%
43 1
0.4%
40 1
0.4%
29 1
0.4%
26 2
0.7%
18 1
0.4%
17 1
0.4%
16 2
0.7%
10 2
0.7%

2015
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2509225
Minimum0
Maximum384
Zeros111
Zeros (%)41.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T18:42:32.922619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile13.5
Maximum384
Range384
Interquartile range (IQR)2

Descriptive statistics

Standard deviation24.857143
Coefficient of variation (CV)5.8474702
Kurtosis204.57408
Mean4.2509225
Median Absolute Deviation (MAD)1
Skewness13.661279
Sum1152
Variance617.87755
MonotonicityNot monotonic
2023-12-12T18:42:33.068918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 111
41.0%
1 74
27.3%
2 30
 
11.1%
3 14
 
5.2%
4 12
 
4.4%
6 5
 
1.8%
7 3
 
1.1%
17 3
 
1.1%
5 3
 
1.1%
15 2
 
0.7%
Other values (12) 14
 
5.2%
ValueCountFrequency (%)
0 111
41.0%
1 74
27.3%
2 30
 
11.1%
3 14
 
5.2%
4 12
 
4.4%
5 3
 
1.1%
6 5
 
1.8%
7 3
 
1.1%
8 2
 
0.7%
11 2
 
0.7%
ValueCountFrequency (%)
384 1
 
0.4%
108 1
 
0.4%
61 1
 
0.4%
51 1
 
0.4%
40 1
 
0.4%
26 1
 
0.4%
24 1
 
0.4%
19 1
 
0.4%
18 1
 
0.4%
17 3
1.1%

2016
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3653137
Minimum0
Maximum575
Zeros78
Zeros (%)28.8%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T18:42:33.227694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile14.5
Maximum575
Range575
Interquartile range (IQR)3

Descriptive statistics

Standard deviation36.769393
Coefficient of variation (CV)5.776525
Kurtosis214.34809
Mean6.3653137
Median Absolute Deviation (MAD)1
Skewness14.050494
Sum1725
Variance1351.9883
MonotonicityNot monotonic
2023-12-12T18:42:33.369810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 78
28.8%
1 58
21.4%
2 41
15.1%
3 28
 
10.3%
4 17
 
6.3%
5 8
 
3.0%
8 6
 
2.2%
7 6
 
2.2%
6 4
 
1.5%
9 4
 
1.5%
Other values (16) 21
 
7.7%
ValueCountFrequency (%)
0 78
28.8%
1 58
21.4%
2 41
15.1%
3 28
 
10.3%
4 17
 
6.3%
5 8
 
3.0%
6 4
 
1.5%
7 6
 
2.2%
8 6
 
2.2%
9 4
 
1.5%
ValueCountFrequency (%)
575 1
 
0.4%
138 1
 
0.4%
90 1
 
0.4%
65 1
 
0.4%
54 1
 
0.4%
47 1
 
0.4%
45 1
 
0.4%
43 1
 
0.4%
28 1
 
0.4%
16 3
1.1%

Interactions

2023-12-12T18:42:26.968767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:14.938616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:15.959014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:17.157936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:18.235733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:19.326893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:20.454542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:21.854903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:23.077814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:24.396072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:25.851121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:27.061177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:15.014937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:16.088946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:17.241514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:18.323994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:19.408427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:20.544522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:22.000175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:23.183527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:24.659008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:25.954157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:27.155223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:15.090951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:16.200400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:17.325077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:18.411503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:19.507119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:20.625607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:22.131442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:23.310541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:24.823788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:26.040033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:27.243329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:15.176359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:16.311801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:17.426386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:18.507570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:19.626903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:20.718565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:22.267215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:23.444909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:24.941000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:26.142965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:27.339180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:15.267753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:16.452145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:17.515745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:18.621913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:19.724991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:20.828557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:22.374304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:23.531819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:25.061793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:26.261511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:27.447596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:15.347522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:16.542154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:17.616718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:18.710777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:19.813093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:20.949040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:22.472730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:23.632174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:25.155078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:26.363020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:27.550184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:15.439463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:16.652991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:17.727310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:18.824431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:19.912947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:21.056763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:22.571547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:23.743100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:25.269517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:26.456126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:27.653170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:15.546518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:16.777386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:17.838693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:18.923245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:20.009657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:21.146244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:22.667717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:23.851452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:25.388742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:26.555494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:27.765024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:15.635885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:16.876347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:17.933125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:19.016804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:20.103560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:21.249008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:22.768048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:23.942017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:25.499306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:26.641782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:27.887755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:15.729318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:16.971574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:18.028695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:19.114489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:20.219293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:21.650179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:22.873398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:24.064818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:25.604096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:26.751583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:27.992714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:15.832010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:17.061054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:18.132953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:19.230078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:20.329473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:21.758875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:22.985297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:24.173102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:25.723301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:42:26.861234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:42:33.522819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도 시도20062007200820092010201120122013201420152016
연도\n 시도1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20061.0001.0000.8680.8410.9970.9770.8681.0000.8680.8010.8680.801
20071.0000.8681.0000.9921.0000.8201.0000.9980.9950.9870.9950.987
20081.0000.8410.9921.0000.8010.8010.9920.9960.9920.9960.9920.996
20091.0000.9971.0000.8011.0000.9911.0000.8610.8200.7700.8200.770
20101.0000.9770.8200.8010.9911.0000.8200.7700.8200.8610.8200.861
20111.0000.8681.0000.9921.0000.8201.0000.9980.9950.9870.9950.987
20121.0001.0000.9980.9960.8610.7700.9981.0000.9980.9900.9980.990
20131.0000.8680.9950.9920.8200.8200.9950.9981.0000.9981.0000.998
20141.0000.8010.9870.9960.7700.8610.9870.9900.9981.0000.9981.000
20151.0000.8680.9950.9920.8200.8200.9950.9981.0000.9981.0000.998
20161.0000.8010.9870.9960.7700.8610.9870.9900.9981.0000.9981.000
2023-12-12T18:42:33.694067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
20062007200820092010201120122013201420152016
20061.0000.5240.4640.4630.4370.4210.4200.3790.3440.3490.312
20070.5241.0000.5110.5150.5140.4600.4800.5070.4820.3760.416
20080.4640.5111.0000.5140.4510.4180.4900.3550.3790.3560.354
20090.4630.5150.5141.0000.4870.4090.4240.3260.2780.3530.310
20100.4370.5140.4510.4871.0000.4440.3900.4700.3650.3330.326
20110.4210.4600.4180.4090.4441.0000.5300.5360.4450.4300.437
20120.4200.4800.4900.4240.3900.5301.0000.5300.4680.4590.489
20130.3790.5070.3550.3260.4700.5360.5301.0000.5820.4990.550
20140.3440.4820.3790.2780.3650.4450.4680.5821.0000.5210.515
20150.3490.3760.3560.3530.3330.4300.4590.4990.5211.0000.562
20160.3120.4160.3540.3100.3260.4370.4890.5500.5150.5621.000

Missing values

2023-12-12T18:42:28.464926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:42:28.665783image/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.
2023-12-12T18:42:28.793814image/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

연도 시도Unnamed: 1Unnamed: 2Unnamed: 320062007200820092010201120122013201420152016
0전국<NA><NA><NA>422450375334473370364527344384575
1서울<NA>서울<NA>2418252328201720161828
2<NA><NA>강남구<NA>20201101211
3<NA><NA>강동구<NA>12121210000
4<NA><NA>강북구<NA>10220021001
5<NA><NA>강서구<NA>21204311104
6<NA><NA>관악구<NA>11114221003
7<NA><NA>광진구<NA>00000200031
8<NA><NA>구로구<NA>00210011012
9<NA><NA>금천구<NA>00000013000
연도 시도Unnamed: 1Unnamed: 2Unnamed: 320062007200820092010201120122013201420152016
261<NA><NA>창원시 마산합포구<NA>00000012238
262<NA><NA>창원시 마산회원구<NA>00000112115
263<NA><NA>창원시 성산구<NA>00000000010
264<NA><NA>창원시 의창구<NA>00000010012
265<NA><NA>창원시 진해구<NA>00000000200
266제주<NA>제주<NA>00000112203
267<NA><NA>서귀포시<NA>00000111102
268<NA><NA>제주시<NA>00000001101
269세종<NA>세종<NA>00000032621
270<NA><NA>세종시<NA>00000032621