Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory72.3 B

Variable types

Numeric7
Text1

Dataset

Description샘플 데이터
Author지디에스컨설팅그룹
URLhttps://www.bigdata-environment.kr/user/data_market/detail.do?id=7801b670-2dff-11ea-9713-eb3e5186fb38

Alerts

행정동 코드 is highly overall correlated with 취약자 수 and 5 other fieldsHigh correlation
취약자 수 is highly overall correlated with 행정동 코드 and 3 other fieldsHigh correlation
취약자 백분위 수 is highly overall correlated with 행정동 코드 and 3 other fieldsHigh correlation
미세먼지 지수 값 is highly overall correlated with 행정동 코드 and 3 other fieldsHigh correlation
미세먼지 백분위 수 is highly overall correlated with 행정동 코드 and 3 other fieldsHigh correlation
강수량 지수 값 is highly overall correlated with 행정동 코드 and 5 other fieldsHigh correlation
강수량 백분위 수 is highly overall correlated with 행정동 코드 and 5 other fieldsHigh correlation
행정동 코드 has unique valuesUnique
행정동 명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:09:22.638704
Analysis finished2023-12-10 13:09:31.854732
Duration9.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동 코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1103705.1
Minimum1101053
Maximum1107060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:09:31.993816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101053
5-th percentile1101057.9
Q11102066.5
median1104054.5
Q31105062.2
95-th percentile1107052.1
Maximum1107060
Range6007
Interquartile range (IQR)2995.75

Descriptive statistics

Standard deviation1858.4336
Coefficient of variation (CV)0.0016838136
Kurtosis-1.1182606
Mean1103705.1
Median Absolute Deviation (MAD)1983
Skewness0.10907068
Sum1.1037051 × 108
Variance3453775.4
MonotonicityStrictly increasing
2023-12-10T22:09:32.245792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1101053 1
 
1.0%
1104073 1
 
1.0%
1105062 1
 
1.0%
1105061 1
 
1.0%
1105060 1
 
1.0%
1105059 1
 
1.0%
1105058 1
 
1.0%
1105057 1
 
1.0%
1105056 1
 
1.0%
1105055 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1101053 1
1.0%
1101054 1
1.0%
1101055 1
1.0%
1101056 1
1.0%
1101057 1
1.0%
1101058 1
1.0%
1101060 1
1.0%
1101061 1
1.0%
1101063 1
1.0%
1101064 1
1.0%
ValueCountFrequency (%)
1107060 1
1.0%
1107059 1
1.0%
1107057 1
1.0%
1107055 1
1.0%
1107054 1
1.0%
1107052 1
1.0%
1106091 1
1.0%
1106090 1
1.0%
1106089 1
1.0%
1106088 1
1.0%

행정동 명
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:09:32.739794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length3.89
Min length2

Characters and Unicode

Total characters389
Distinct characters102
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row사직동
2nd row삼청동
3rd row부암동
4th row평창동
5th row무악동
ValueCountFrequency (%)
사직동 1
 
1.0%
왕십리도선동 1
 
1.0%
구의2동 1
 
1.0%
구의1동 1
 
1.0%
능동 1
 
1.0%
중곡4동 1
 
1.0%
중곡3동 1
 
1.0%
중곡2동 1
 
1.0%
중곡1동 1
 
1.0%
군자동 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T22:09:33.680811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 101
26.0%
2 23
 
5.9%
1 21
 
5.4%
가 11
 
2.8%
이 7
 
1.8%
3 7
 
1.8%
자 6
 
1.5%
신 6
 
1.5%
수 6
 
1.5%
로 6
 
1.5%
Other values (92) 195
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 323
83.0%
Decimal Number 61
 
15.7%
Other Punctuation 5
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 101
31.3%
가 11
 
3.4%
이 7
 
2.2%
자 6
 
1.9%
신 6
 
1.9%
수 6
 
1.9%
로 6
 
1.9%
양 5
 
1.5%
청 5
 
1.5%
중 5
 
1.5%
Other values (84) 165
51.1%
Decimal Number
ValueCountFrequency (%)
2 23
37.7%
1 21
34.4%
3 7
 
11.5%
4 5
 
8.2%
5 3
 
4.9%
6 1
 
1.6%
7 1
 
1.6%
Other Punctuation
ValueCountFrequency (%)
· 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 323
83.0%
Common 66
 
17.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 101
31.3%
가 11
 
3.4%
이 7
 
2.2%
자 6
 
1.9%
신 6
 
1.9%
수 6
 
1.9%
로 6
 
1.9%
양 5
 
1.5%
청 5
 
1.5%
중 5
 
1.5%
Other values (84) 165
51.1%
Common
ValueCountFrequency (%)
2 23
34.8%
1 21
31.8%
3 7
 
10.6%
4 5
 
7.6%
· 5
 
7.6%
5 3
 
4.5%
6 1
 
1.5%
7 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 323
83.0%
ASCII 61
 
15.7%
None 5
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 101
31.3%
가 11
 
3.4%
이 7
 
2.2%
자 6
 
1.9%
신 6
 
1.9%
수 6
 
1.9%
로 6
 
1.9%
양 5
 
1.5%
청 5
 
1.5%
중 5
 
1.5%
Other values (84) 165
51.1%
ASCII
ValueCountFrequency (%)
2 23
37.7%
1 21
34.4%
3 7
 
11.5%
4 5
 
8.2%
5 3
 
4.9%
6 1
 
1.6%
7 1
 
1.6%
None
ValueCountFrequency (%)
· 5
100.0%

취약자 수
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3927.46
Minimum311
Maximum9612
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:09:34.109261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum311
5-th percentile1165.5
Q12371.5
median3696.5
Q35519.25
95-th percentile7451.85
Maximum9612
Range9301
Interquartile range (IQR)3147.75

Descriptive statistics

Standard deviation2077.9943
Coefficient of variation (CV)0.52909368
Kurtosis-0.40987764
Mean3927.46
Median Absolute Deviation (MAD)1420
Skewness0.4785411
Sum392746
Variance4318060.1
MonotonicityNot monotonic
2023-12-10T22:09:34.413156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6176 2
 
2.0%
2565 1
 
1.0%
4856 1
 
1.0%
4377 1
 
1.0%
2317 1
 
1.0%
6961 1
 
1.0%
3706 1
 
1.0%
4671 1
 
1.0%
3256 1
 
1.0%
4206 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
311 1
1.0%
370 1
1.0%
704 1
1.0%
838 1
1.0%
1042 1
1.0%
1172 1
1.0%
1219 1
1.0%
1244 1
1.0%
1255 1
1.0%
1258 1
1.0%
ValueCountFrequency (%)
9612 1
1.0%
8781 1
1.0%
8318 1
1.0%
8098 1
1.0%
7563 1
1.0%
7446 1
1.0%
7406 1
1.0%
7289 1
1.0%
6961 1
1.0%
6924 1
1.0%

취약자 백분위 수
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.6
Minimum1
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:09:34.692224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.8
Q142.75
median58.5
Q376
95-th percentile88
Maximum94
Range93
Interquartile range (IQR)33.25

Descriptive statistics

Standard deviation22.366552
Coefficient of variation (CV)0.39516875
Kurtosis-0.40803448
Mean56.6
Median Absolute Deviation (MAD)16.5
Skewness-0.43939665
Sum5660
Variance500.26263
MonotonicityNot monotonic
2023-12-10T22:09:34.929576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 6
 
6.0%
58 4
 
4.0%
85 3
 
3.0%
81 3
 
3.0%
68 3
 
3.0%
22 3
 
3.0%
34 3
 
3.0%
42 3
 
3.0%
44 3
 
3.0%
82 3
 
3.0%
Other values (47) 66
66.0%
ValueCountFrequency (%)
1 2
2.0%
6 1
 
1.0%
9 1
 
1.0%
16 1
 
1.0%
20 1
 
1.0%
21 1
 
1.0%
22 3
3.0%
25 1
 
1.0%
27 1
 
1.0%
31 2
2.0%
ValueCountFrequency (%)
94 1
 
1.0%
92 1
 
1.0%
91 1
 
1.0%
90 1
 
1.0%
88 2
2.0%
87 2
2.0%
85 3
3.0%
84 1
 
1.0%
82 3
3.0%
81 3
3.0%

미세먼지 지수 값
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.37269
Minimum18.399
Maximum680.934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:09:35.114277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.399
5-th percentile21.759
Q121.759
median24.967
Q365.70825
95-th percentile235.979
Maximum680.934
Range662.535
Interquartile range (IQR)43.94925

Descriptive statistics

Standard deviation100.96141
Coefficient of variation (CV)1.5211288
Kurtosis17.78202
Mean66.37269
Median Absolute Deviation (MAD)3.208
Skewness3.81274
Sum6637.269
Variance10193.206
MonotonicityNot monotonic
2023-12-10T22:09:35.331157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
21.759 22
22.0%
22.127 12
12.0%
24.967 12
12.0%
23.673 9
 
9.0%
26.207 7
 
7.0%
18.399 4
 
4.0%
104.827 4
 
4.0%
281.766 2
 
2.0%
234.805 2
 
2.0%
131.034 2
 
2.0%
Other values (20) 24
24.0%
ValueCountFrequency (%)
18.399 4
 
4.0%
21.759 22
22.0%
22.127 12
12.0%
23.48 1
 
1.0%
23.673 9
9.0%
24.967 12
12.0%
26.207 7
 
7.0%
43.518 2
 
2.0%
46.726 1
 
1.0%
47.346 1
 
1.0%
ValueCountFrequency (%)
680.934 1
1.0%
540.051 1
1.0%
281.766 2
2.0%
258.285 1
1.0%
234.805 2
2.0%
231.362 1
1.0%
226.199 1
1.0%
164.363 1
1.0%
140.883 2
2.0%
131.034 2
2.0%

미세먼지 백분위 수
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.95
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:09:35.548406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median11
Q346
95-th percentile68.2
Maximum100
Range99
Interquartile range (IQR)43

Descriptive statistics

Standard deviation25.184722
Coefficient of variation (CV)1.0973735
Kurtosis0.29055886
Mean22.95
Median Absolute Deviation (MAD)8
Skewness1.1594658
Sum2295
Variance634.2702
MonotonicityNot monotonic
2023-12-10T22:09:35.741310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3 22
22.0%
5 12
12.0%
12 9
 
9.0%
15 7
 
7.0%
8 7
 
7.0%
1 4
 
4.0%
52 4
 
4.0%
46 3
 
3.0%
11 3
 
3.0%
7 3
 
3.0%
Other values (17) 26
26.0%
ValueCountFrequency (%)
1 4
 
4.0%
3 22
22.0%
5 12
12.0%
7 3
 
3.0%
8 7
 
7.0%
11 3
 
3.0%
12 9
9.0%
15 7
 
7.0%
36 2
 
2.0%
37 2
 
2.0%
ValueCountFrequency (%)
100 1
 
1.0%
99 1
 
1.0%
76 2
2.0%
72 1
 
1.0%
68 3
3.0%
67 1
 
1.0%
60 1
 
1.0%
57 2
2.0%
56 2
2.0%
54 1
 
1.0%

강수량 지수 값
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.62232
Minimum2.178
Maximum86.458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:09:35.927853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.178
5-th percentile2.178
Q12.178
median2.38
Q37.2905
95-th percentile29.9621
Maximum86.458
Range84.28
Interquartile range (IQR)5.1125

Descriptive statistics

Standard deviation12.878872
Coefficient of variation (CV)1.6896263
Kurtosis18.365886
Mean7.62232
Median Absolute Deviation (MAD)0.202
Skewness3.908196
Sum762.232
Variance165.86534
MonotonicityNot monotonic
2023-12-10T22:09:36.205609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2.178 28
28.0%
2.38 22
22.0%
2.275 9
 
9.0%
2.538 7
 
7.0%
10.151 4
 
4.0%
35.776 2
 
2.0%
17.888 2
 
2.0%
11.925 2
 
2.0%
4.76 2
 
2.0%
29.813 2
 
2.0%
Other values (18) 20
20.0%
ValueCountFrequency (%)
2.178 28
28.0%
2.275 9
 
9.0%
2.38 22
22.0%
2.538 7
 
7.0%
2.981 1
 
1.0%
4.549 1
 
1.0%
4.559 1
 
1.0%
4.76 2
 
2.0%
4.881 1
 
1.0%
5.075 2
 
2.0%
ValueCountFrequency (%)
86.458 1
1.0%
68.571 1
1.0%
35.776 2
2.0%
32.795 1
1.0%
29.813 2
2.0%
28.611 1
1.0%
26.807 1
1.0%
20.869 1
1.0%
17.888 2
2.0%
12.689 2
2.0%

강수량 백분위 수
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.02
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:09:36.432173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14
Q122
median33
Q351
95-th percentile88.25
Maximum100
Range90
Interquartile range (IQR)29

Descriptive statistics

Standard deviation21.690188
Coefficient of variation (CV)0.5558736
Kurtosis1.1154066
Mean39.02
Median Absolute Deviation (MAD)11
Skewness1.2557572
Sum3902
Variance470.46424
MonotonicityNot monotonic
2023-12-10T22:09:36.747351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
33 22
22.0%
22 10
 
10.0%
14 7
 
7.0%
31 7
 
7.0%
34 5
 
5.0%
55 5
 
5.0%
45 5
 
5.0%
20 3
 
3.0%
58 3
 
3.0%
15 2
 
2.0%
Other values (20) 31
31.0%
ValueCountFrequency (%)
10 1
 
1.0%
12 1
 
1.0%
14 7
7.0%
15 2
 
2.0%
16 1
 
1.0%
19 2
 
2.0%
20 3
 
3.0%
21 1
 
1.0%
22 10
10.0%
30 2
 
2.0%
ValueCountFrequency (%)
100 2
2.0%
95 2
2.0%
93 1
 
1.0%
88 2
2.0%
87 1
 
1.0%
84 1
 
1.0%
72 1
 
1.0%
67 2
2.0%
58 3
3.0%
57 2
2.0%

Interactions

2023-12-10T22:09:30.173512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:23.137216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:24.587611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:25.703017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:26.729543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:28.108007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:29.166111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:30.313300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:23.399920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:24.738434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:25.871053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:26.880435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:28.301331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:29.317458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:30.455078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:23.559196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:24.877225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:26.020719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:27.048382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:28.456068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:29.450123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:30.567422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:23.693109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:25.019088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:26.200109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:27.187595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:28.583869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:29.610816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:30.720459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:23.905154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:25.220298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:26.347126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:27.369982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:28.726062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:29.787002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:30.871249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:24.172660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:25.408786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:26.484324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:27.545794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:28.859409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:29.918194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:31.005099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:24.383081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:25.565783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:26.603450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:27.871067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:29.004076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:30.041725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:09:36.883986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드행정동 명취약자 수취약자 백분위 수미세먼지 지수 값미세먼지 백분위 수강수량 지수 값강수량 백분위 수
행정동 코드1.0001.0000.5560.4980.3960.7390.3040.691
행정동 명1.0001.0001.0001.0001.0001.0001.0001.000
취약자 수0.5561.0001.0000.9680.4700.2800.4690.333
취약자 백분위 수0.4981.0000.9681.0000.6140.5280.6210.484
미세먼지 지수 값0.3961.0000.4700.6141.0000.8840.9940.861
미세먼지 백분위 수0.7391.0000.2800.5280.8841.0000.8550.942
강수량 지수 값0.3041.0000.4690.6210.9940.8551.0000.859
강수량 백분위 수0.6911.0000.3330.4840.8610.9420.8591.000
2023-12-10T22:09:37.104727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드취약자 수취약자 백분위 수미세먼지 지수 값미세먼지 백분위 수강수량 지수 값강수량 백분위 수
행정동 코드1.0000.7170.715-0.506-0.506-0.753-0.719
취약자 수0.7171.0001.000-0.428-0.431-0.559-0.543
취약자 백분위 수0.7151.0001.000-0.426-0.430-0.557-0.540
미세먼지 지수 값-0.506-0.428-0.4261.0000.9990.6950.649
미세먼지 백분위 수-0.506-0.431-0.4300.9991.0000.6950.648
강수량 지수 값-0.753-0.559-0.5570.6950.6951.0000.989
강수량 백분위 수-0.719-0.543-0.5400.6490.6480.9891.000

Missing values

2023-12-10T22:09:31.196620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:09:31.772426image/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

행정동 코드행정동 명취약자 수취약자 백분위 수미세먼지 지수 값미세먼지 백분위 수강수량 지수 값강수량 백분위 수
01101053사직동256545281.7667635.77695
11101054삼청동8389164.3636020.86972
21101055부암동26694776.766477.86352
31101056평창동50707253.286404.88145
41101057무악동23864323.4872.98136
51101058교남동124422140.8835717.88867
61101060가회동12582293.9225111.92557
71101061종로1·2·3·4가동177334680.93410086.458100
81101063종로5·6가동125522110.5175412.50258
91101064이화동17963466.999467.74251
행정동 코드행정동 명취약자 수취약자 백분위 수미세먼지 지수 값미세먼지 백분위 수강수량 지수 값강수량 백분위 수
901106088장안2동83189122.12752.17822
911106089이문1동66268418.39912.17822
921106090이문2동58277818.39912.17822
931106091답십리1동62678221.75932.3833
941107052면목2동61768122.12752.17822
951107054면목4동52167322.12752.17822
961107055면목5동27194722.12752.17822
971107057면목7동55207622.12752.17822
981107059상봉1동61768118.39912.17822
991107060상봉2동37645918.39912.17822