Overview

Dataset statistics

Number of variables10
Number of observations42
Missing cells155
Missing cells (%)36.9%
Duplicate rows2
Duplicate rows (%)4.8%
Total size in memory3.7 KiB
Average record size in memory91.1 B

Variable types

Unsupported2
Numeric8

Dataset

Description법정감염병 중 3군에 속하는 모기매개감염병인 말라리아의 민간인 연령별 발생현황. (단위: 건) * 강원도 철원군, 인천 강화군 지역
Author질병관리청
URLhttps://www.data.go.kr/data/15033737/fileData.do

Alerts

Dataset has 2 (4.8%) duplicate rowsDuplicates
연도 is highly overall correlated with Unnamed: 2 and 6 other fieldsHigh correlation
Unnamed: 2 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
Unnamed: 3 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
Unnamed: 4 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
Unnamed: 5 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
Unnamed: 6 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
Unnamed: 7 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
Unnamed: 8 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
연도 has 8 (19.0%) missing valuesMissing
Unnamed: 2 has 18 (42.9%) missing valuesMissing
Unnamed: 3 has 22 (52.4%) missing valuesMissing
Unnamed: 4 has 22 (52.4%) missing valuesMissing
Unnamed: 5 has 21 (50.0%) missing valuesMissing
Unnamed: 6 has 20 (47.6%) missing valuesMissing
Unnamed: 7 has 18 (42.9%) missing valuesMissing
Unnamed: 8 has 24 (57.1%) missing valuesMissing
Unnamed: 9 has 2 (4.8%) missing valuesMissing
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 15:20:50.297191
Analysis finished2023-12-12 15:20:57.997163
Duration7.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size468.0 B

연도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)41.2%
Missing8
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean69.088235
Minimum1
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T00:20:58.046013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2.5
Q37.75
95-th percentile94.8
Maximum2010
Range2009
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation343.745
Coefficient of variation (CV)4.9754492
Kurtosis33.656792
Mean69.088235
Median Absolute Deviation (MAD)1.5
Skewness5.7892743
Sum2349
Variance118160.63
MonotonicityNot monotonic
2023-12-13T00:20:58.153201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 10
23.8%
1 7
16.7%
3 2
 
4.8%
9 2
 
4.8%
11 2
 
4.8%
6 2
 
4.8%
4 2
 
4.8%
2010 1
 
2.4%
85 1
 
2.4%
7 1
 
2.4%
Other values (4) 4
 
9.5%
(Missing) 8
19.0%
ValueCountFrequency (%)
1 7
16.7%
2 10
23.8%
3 2
 
4.8%
4 2
 
4.8%
5 1
 
2.4%
6 2
 
4.8%
7 1
 
2.4%
8 1
 
2.4%
9 2
 
4.8%
11 2
 
4.8%
ValueCountFrequency (%)
2010 1
2.4%
113 1
2.4%
85 1
2.4%
28 1
2.4%
11 2
4.8%
9 2
4.8%
8 1
2.4%
7 1
2.4%
6 2
4.8%
5 1
2.4%

Unnamed: 2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)45.8%
Missing18
Missing (%)42.9%
Infinite0
Infinite (%)0.0%
Mean90.666667
Minimum1
Maximum2011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T00:20:58.269357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q36.25
95-th percentile53.05
Maximum2011
Range2010
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation409.24211
Coefficient of variation (CV)4.5136997
Kurtosis23.943227
Mean90.666667
Median Absolute Deviation (MAD)2
Skewness4.8907496
Sum2176
Variance167479.1
MonotonicityNot monotonic
2023-12-13T00:20:58.373291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 8
19.0%
3 4
 
9.5%
2 3
 
7.1%
7 2
 
4.8%
2011 1
 
2.4%
42 1
 
2.4%
6 1
 
2.4%
5 1
 
2.4%
13 1
 
2.4%
4 1
 
2.4%
(Missing) 18
42.9%
ValueCountFrequency (%)
1 8
19.0%
2 3
 
7.1%
3 4
9.5%
4 1
 
2.4%
5 1
 
2.4%
6 1
 
2.4%
7 2
 
4.8%
13 1
 
2.4%
42 1
 
2.4%
55 1
 
2.4%
ValueCountFrequency (%)
2011 1
 
2.4%
55 1
 
2.4%
42 1
 
2.4%
13 1
 
2.4%
7 2
4.8%
6 1
 
2.4%
5 1
 
2.4%
4 1
 
2.4%
3 4
9.5%
2 3
7.1%

Unnamed: 3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)45.0%
Missing22
Missing (%)52.4%
Infinite0
Infinite (%)0.0%
Mean108.7
Minimum1
Maximum2012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T00:20:58.483034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.75
median3
Q37
95-th percentile151.9
Maximum2012
Range2011
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation448.25663
Coefficient of variation (CV)4.1237961
Kurtosis19.944492
Mean108.7
Median Absolute Deviation (MAD)2
Skewness4.4633974
Sum2174
Variance200934.01
MonotonicityNot monotonic
2023-12-13T00:20:58.590029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 5
 
11.9%
2 4
 
9.5%
3 3
 
7.1%
5 2
 
4.8%
7 2
 
4.8%
2012 1
 
2.4%
52 1
 
2.4%
10 1
 
2.4%
54 1
 
2.4%
(Missing) 22
52.4%
ValueCountFrequency (%)
1 5
11.9%
2 4
9.5%
3 3
7.1%
5 2
 
4.8%
7 2
 
4.8%
10 1
 
2.4%
52 1
 
2.4%
54 1
 
2.4%
2012 1
 
2.4%
ValueCountFrequency (%)
2012 1
 
2.4%
54 1
 
2.4%
52 1
 
2.4%
10 1
 
2.4%
7 2
 
4.8%
5 2
 
4.8%
3 3
7.1%
2 4
9.5%
1 5
11.9%

Unnamed: 4
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)45.0%
Missing22
Missing (%)52.4%
Infinite0
Infinite (%)0.0%
Mean106.35
Minimum1
Maximum2013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T00:20:58.699266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2.5
Q35
95-th percentile136.75
Maximum2013
Range2012
Interquartile range (IQR)4

Descriptive statistics

Standard deviation448.89036
Coefficient of variation (CV)4.2208779
Kurtosis19.976791
Mean106.35
Median Absolute Deviation (MAD)1.5
Skewness4.4684641
Sum2127
Variance201502.56
MonotonicityNot monotonic
2023-12-13T00:20:58.803516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 8
 
19.0%
5 3
 
7.1%
4 2
 
4.8%
2 2
 
4.8%
2013 1
 
2.4%
31 1
 
2.4%
3 1
 
2.4%
7 1
 
2.4%
38 1
 
2.4%
(Missing) 22
52.4%
ValueCountFrequency (%)
1 8
19.0%
2 2
 
4.8%
3 1
 
2.4%
4 2
 
4.8%
5 3
 
7.1%
7 1
 
2.4%
31 1
 
2.4%
38 1
 
2.4%
2013 1
 
2.4%
ValueCountFrequency (%)
2013 1
 
2.4%
38 1
 
2.4%
31 1
 
2.4%
7 1
 
2.4%
5 3
 
7.1%
4 2
 
4.8%
3 1
 
2.4%
2 2
 
4.8%
1 8
19.0%

Unnamed: 5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)42.9%
Missing21
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean100.47619
Minimum1
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T00:20:58.910768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile32
Maximum2014
Range2013
Interquartile range (IQR)3

Descriptive statistics

Standard deviation438.52099
Coefficient of variation (CV)4.364427
Kurtosis20.982772
Mean100.47619
Median Absolute Deviation (MAD)1
Skewness4.5799061
Sum2110
Variance192300.66
MonotonicityNot monotonic
2023-12-13T00:20:59.012504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 10
23.8%
3 3
 
7.1%
2 2
 
4.8%
2014 1
 
2.4%
26 1
 
2.4%
4 1
 
2.4%
5 1
 
2.4%
6 1
 
2.4%
32 1
 
2.4%
(Missing) 21
50.0%
ValueCountFrequency (%)
1 10
23.8%
2 2
 
4.8%
3 3
 
7.1%
4 1
 
2.4%
5 1
 
2.4%
6 1
 
2.4%
26 1
 
2.4%
32 1
 
2.4%
2014 1
 
2.4%
ValueCountFrequency (%)
2014 1
 
2.4%
32 1
 
2.4%
26 1
 
2.4%
6 1
 
2.4%
5 1
 
2.4%
4 1
 
2.4%
3 3
 
7.1%
2 2
 
4.8%
1 10
23.8%

Unnamed: 6
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)36.4%
Missing20
Missing (%)47.6%
Infinite0
Infinite (%)0.0%
Mean95.954545
Minimum1
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T00:20:59.128699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33.75
95-th percentile31.7
Maximum2015
Range2014
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation428.70108
Coefficient of variation (CV)4.4677517
Kurtosis21.981981
Mean95.954545
Median Absolute Deviation (MAD)1
Skewness4.6876825
Sum2111
Variance183784.62
MonotonicityNot monotonic
2023-12-13T00:20:59.255001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 10
23.8%
2 4
 
9.5%
3 2
 
4.8%
4 2
 
4.8%
2015 1
 
2.4%
26 1
 
2.4%
6 1
 
2.4%
32 1
 
2.4%
(Missing) 20
47.6%
ValueCountFrequency (%)
1 10
23.8%
2 4
 
9.5%
3 2
 
4.8%
4 2
 
4.8%
6 1
 
2.4%
26 1
 
2.4%
32 1
 
2.4%
2015 1
 
2.4%
ValueCountFrequency (%)
2015 1
 
2.4%
32 1
 
2.4%
26 1
 
2.4%
6 1
 
2.4%
4 2
 
4.8%
3 2
 
4.8%
2 4
 
9.5%
1 10
23.8%

Unnamed: 7
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)29.2%
Missing18
Missing (%)42.9%
Infinite0
Infinite (%)0.0%
Mean88.25
Minimum1
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T00:20:59.376387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile32.2
Maximum2016
Range2015
Interquartile range (IQR)2

Descriptive statistics

Standard deviation410.68323
Coefficient of variation (CV)4.6536344
Kurtosis23.980276
Mean88.25
Median Absolute Deviation (MAD)1
Skewness4.8961046
Sum2118
Variance168660.72
MonotonicityNot monotonic
2023-12-13T00:20:59.496320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 10
23.8%
2 6
 
14.3%
3 4
 
9.5%
2016 1
 
2.4%
22 1
 
2.4%
12 1
 
2.4%
34 1
 
2.4%
(Missing) 18
42.9%
ValueCountFrequency (%)
1 10
23.8%
2 6
14.3%
3 4
 
9.5%
12 1
 
2.4%
22 1
 
2.4%
34 1
 
2.4%
2016 1
 
2.4%
ValueCountFrequency (%)
2016 1
 
2.4%
34 1
 
2.4%
22 1
 
2.4%
12 1
 
2.4%
3 4
 
9.5%
2 6
14.3%
1 10
23.8%

Unnamed: 8
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)38.9%
Missing24
Missing (%)57.1%
Infinite0
Infinite (%)0.0%
Mean115.55556
Minimum1
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T00:20:59.601671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q33
95-th percentile320.4
Maximum2017
Range2016
Interquartile range (IQR)2

Descriptive statistics

Standard deviation474.57067
Coefficient of variation (CV)4.1068616
Kurtosis17.994105
Mean115.55556
Median Absolute Deviation (MAD)0.5
Skewness4.2416585
Sum2080
Variance225217.32
MonotonicityNot monotonic
2023-12-13T00:20:59.717146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 9
 
21.4%
2 3
 
7.1%
3 2
 
4.8%
2017 1
 
2.4%
16 1
 
2.4%
5 1
 
2.4%
21 1
 
2.4%
(Missing) 24
57.1%
ValueCountFrequency (%)
1 9
21.4%
2 3
 
7.1%
3 2
 
4.8%
5 1
 
2.4%
16 1
 
2.4%
21 1
 
2.4%
2017 1
 
2.4%
ValueCountFrequency (%)
2017 1
 
2.4%
21 1
 
2.4%
16 1
 
2.4%
5 1
 
2.4%
3 2
 
4.8%
2 3
 
7.1%
1 9
21.4%

Unnamed: 9
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2
Missing (%)4.8%
Memory size468.0 B

Interactions

2023-12-13T00:20:56.373633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:50.517097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:51.143818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:52.083138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:52.843704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:53.614843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:54.496535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:55.410566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:56.493633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:50.588290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:51.220190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:52.170105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:52.945989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:53.705802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:54.614495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:55.518750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:56.608580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:50.661792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:51.296177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:52.256780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:53.065338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:53.806612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:54.725993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:55.652896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:56.745619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:50.740245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:51.374093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:52.340425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:53.167005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:53.913418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:54.846182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:55.775431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:56.854989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:50.813681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:51.458296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:52.439821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:53.246767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:54.045106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:54.978841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:55.890606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:56.980414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:50.888637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:51.538066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:52.544070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:53.329298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:54.157514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:55.081971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:55.991763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:57.085646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:50.972615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:51.906675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:52.648811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:53.419847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:54.269810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:55.194649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:56.127711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:57.492778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:51.054087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:51.994357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:52.734268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:53.521221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:54.364921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:55.300555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:20:56.268588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:20:59.804498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
연도1.0000.6320.6180.6180.6180.6180.6320.605
Unnamed: 20.6321.0000.6120.6120.6050.5970.6120.597
Unnamed: 30.6180.6121.0000.5760.5760.6050.5970.576
Unnamed: 40.6180.6120.5761.0000.5760.5870.5760.587
Unnamed: 50.6180.6050.5760.5761.0000.5620.5970.562
Unnamed: 60.6180.5970.6050.5870.5621.0000.5970.587
Unnamed: 70.6320.6120.5970.5760.5970.5971.0000.576
Unnamed: 80.6050.5970.5760.5870.5620.5870.5761.000
2023-12-13T00:20:59.925576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
연도1.0000.7470.7220.6410.7940.5620.6270.663
Unnamed: 20.7471.0000.5770.6770.7580.6500.8060.817
Unnamed: 30.7220.5771.0000.7020.6810.7330.5830.423
Unnamed: 40.6410.6770.7021.0000.8580.9940.6570.482
Unnamed: 50.7940.7580.6810.8581.0000.9070.8030.804
Unnamed: 60.5620.6500.7330.9940.9071.0000.6350.564
Unnamed: 70.6270.8060.5830.6570.8030.6351.0000.835
Unnamed: 80.6630.8170.4230.4820.8040.5640.8351.000

Missing values

2023-12-13T00:20:57.610765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:20:57.755355image/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.
2023-12-13T00:20:57.891304image/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

Unnamed: 0연도Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
0지역별/연령별20102011201220132014201520162017총합계
1인천<NA><NA><NA><NA><NA><NA><NA><NA>NaN
2강화군8542523126262216300
35<NA>11<NA><NA>11<NA>4
4103<NA><NA><NA><NA>1<NA><NA>4
51512<NA>11<NA>117
620211<NA>1<NA>117
7252<NA>3<NA><NA>32<NA>10
830211<NA><NA><NA><NA><NA>4
9357121211<NA>15
Unnamed: 0연도Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
324532<NA>1<NA>13111
3350231<NA><NA>12110
34552<NA><NA><NA><NA><NA>1<NA>3
356011<NA><NA>1<NA>1<NA>4
366551<NA>1<NA><NA><NA>29
37701<NA><NA><NA><NA><NA>2<NA>3
3875<NA>1<NA>1<NA><NA><NA><NA>2
3980<NA><NA><NA><NA>11<NA>13
4085<NA><NA><NA><NA><NA>1<NA><NA>1
41총합계11355543832323421379

Duplicate rows

Most frequently occurring

연도Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8# duplicates
01<NA><NA><NA><NA><NA><NA><NA>2
1<NA><NA><NA><NA><NA><NA><NA><NA>2