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 3 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 미세먼지 백분위 수 and 2 other fieldsHigh correlation
미세먼지 백분위 수 is highly overall correlated with 미세먼지 지수 값 and 2 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 13:09:53.882225
Analysis finished2023-12-10 13:10:01.768624
Duration7.89 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
2023-12-10T22:10:01.988678image/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
2023-12-10T22:10:02.214549image/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
2023-12-10T22:10:02.712780image/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%
2023-12-10T22:10:03.534446image/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
2023-12-10T22:10:04.349985image/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
2023-12-10T22:10:04.731114image/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%
Mean56.61
Minimum1
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:10:05.022605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.8
Q142.75
median59
Q376
95-th percentile88
Maximum94
Range93
Interquartile range (IQR)33.25

Descriptive statistics

Standard deviation22.367407
Coefficient of variation (CV)0.39511407
Kurtosis-0.40762478
Mean56.61
Median Absolute Deviation (MAD)17
Skewness-0.44070462
Sum5661
Variance500.30091
MonotonicityNot monotonic
2023-12-10T22:10:05.310361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 7
 
7.0%
85 3
 
3.0%
81 3
 
3.0%
68 3
 
3.0%
58 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%
Mean97.00691
Minimum31.557
Maximum953.885
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:10:05.661701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31.557
5-th percentile31.557
Q134.454
median38.05
Q395.005
95-th percentile330.5706
Maximum953.885
Range922.328
Interquartile range (IQR)60.551

Descriptive statistics

Standard deviation141.38579
Coefficient of variation (CV)1.4574817
Kurtosis17.372736
Mean97.00691
Median Absolute Deviation (MAD)6.493
Skewness3.7518278
Sum9700.691
Variance19989.943
MonotonicityNot monotonic
2023-12-10T22:10:05.903439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
31.557 22
22.0%
34.884 12
12.0%
34.454 12
12.0%
38.05 9
 
9.0%
41.992 7
 
7.0%
41.944 4
 
4.0%
167.97 4
 
4.0%
394.711 2
 
2.0%
328.926 2
 
2.0%
209.962 2
 
2.0%
Other values (20) 24
24.0%
ValueCountFrequency (%)
31.557 22
22.0%
32.893 1
 
1.0%
34.454 12
12.0%
34.884 12
12.0%
38.05 9
9.0%
41.944 4
 
4.0%
41.992 7
 
7.0%
63.114 2
 
2.0%
66.011 1
 
1.0%
73.418 1
 
1.0%
ValueCountFrequency (%)
953.885 1
1.0%
756.529 1
1.0%
394.711 2
2.0%
361.818 1
1.0%
328.926 2
2.0%
326.255 1
1.0%
322.248 1
1.0%
230.248 1
1.0%
209.962 2
2.0%
197.355 2
2.0%

미세먼지 백분위 수
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33
Minimum6
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:10:06.113567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7
Q121
median30
Q347
95-th percentile76.2
Maximum100
Range94
Interquartile range (IQR)26

Descriptive statistics

Standard deviation22.705848
Coefficient of variation (CV)0.68805601
Kurtosis0.37481555
Mean33
Median Absolute Deviation (MAD)16
Skewness0.89748396
Sum3300
Variance515.55556
MonotonicityNot monotonic
2023-12-10T22:10:06.345033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
7 19
19.0%
22 12
12.0%
21 12
12.0%
34 9
 
9.0%
30 8
 
8.0%
6 3
 
3.0%
60 3
 
3.0%
57 3
 
3.0%
35 2
 
2.0%
38 2
 
2.0%
Other values (22) 27
27.0%
ValueCountFrequency (%)
6 3
 
3.0%
7 19
19.0%
13 1
 
1.0%
21 12
12.0%
22 12
12.0%
30 8
8.0%
31 1
 
1.0%
34 9
9.0%
35 2
 
2.0%
38 2
 
2.0%
ValueCountFrequency (%)
100 2
2.0%
84 1
 
1.0%
83 1
 
1.0%
80 1
 
1.0%
76 2
2.0%
73 1
 
1.0%
72 1
 
1.0%
63 1
 
1.0%
60 3
3.0%
59 1
 
1.0%

강수량 지수 값
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42366
Minimum0.148
Maximum4.391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:10:06.658092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.148
5-th percentile0.148
Q10.148
median0.149
Q30.44675
95-th percentile1.52155
Maximum4.391
Range4.243
Interquartile range (IQR)0.29875

Descriptive statistics

Standard deviation0.64996258
Coefficient of variation (CV)1.5341608
Kurtosis18.038768
Mean0.42366
Median Absolute Deviation (MAD)0.001
Skewness3.8556344
Sum42.366
Variance0.42245136
MonotonicityNot monotonic
2023-12-10T22:10:06.879196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.148 37
37.0%
0.149 22
22.0%
0.15 7
 
7.0%
0.598 4
 
4.0%
0.298 3
 
3.0%
1.817 2
 
2.0%
0.297 2
 
2.0%
0.908 2
 
2.0%
0.606 2
 
2.0%
0.748 2
 
2.0%
Other values (15) 17
17.0%
ValueCountFrequency (%)
0.148 37
37.0%
0.149 22
22.0%
0.15 7
 
7.0%
0.151 1
 
1.0%
0.297 2
 
2.0%
0.298 3
 
3.0%
0.299 2
 
2.0%
0.446 1
 
1.0%
0.449 1
 
1.0%
0.45 1
 
1.0%
ValueCountFrequency (%)
4.391 1
1.0%
3.482 1
1.0%
1.817 2
2.0%
1.665 1
1.0%
1.514 2
2.0%
1.509 1
1.0%
1.501 1
1.0%
1.06 1
1.0%
0.908 2
2.0%
0.748 2
2.0%

강수량 백분위 수
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.45
Minimum15
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:10:07.091448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile18
Q127.75
median34
Q350.25
95-th percentile76.25
Maximum100
Range85
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation18.212758
Coefficient of variation (CV)0.46166686
Kurtosis1.7457459
Mean39.45
Median Absolute Deviation (MAD)9
Skewness1.3449911
Sum3945
Variance331.70455
MonotonicityNot monotonic
2023-12-10T22:10:07.276747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
34 16
16.0%
33 14
14.0%
35 8
 
8.0%
54 7
 
7.0%
45 4
 
4.0%
27 4
 
4.0%
76 4
 
4.0%
19 4
 
4.0%
44 3
 
3.0%
51 3
 
3.0%
Other values (20) 33
33.0%
ValueCountFrequency (%)
15 2
2.0%
16 1
 
1.0%
17 1
 
1.0%
18 2
2.0%
19 4
4.0%
20 1
 
1.0%
21 3
3.0%
23 2
2.0%
25 2
2.0%
26 3
3.0%
ValueCountFrequency (%)
100 2
 
2.0%
84 2
 
2.0%
81 1
 
1.0%
76 4
4.0%
63 1
 
1.0%
60 2
 
2.0%
57 3
3.0%
54 7
7.0%
51 3
3.0%
50 1
 
1.0%

Interactions

2023-12-10T22:10:00.484456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:54.289687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:55.300947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:56.344237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:57.454588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:58.564196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:59.507274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:00.614396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:54.459591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:55.453592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:56.583504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:57.619136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:58.754036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:59.665329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:00.730517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:54.623904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:55.600665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:56.739401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:57.768708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:58.874132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:59.801793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:00.837733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:54.768128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:55.756593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:56.862429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:57.908981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:58.996287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:59.975804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:00.963447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:54.913827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:55.890590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:56.992476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:58.031479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:59.135575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:00.123865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:01.143013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:55.041875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:56.035893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:57.156669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:58.158893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:59.258974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:00.241928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:01.256510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:55.178974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:56.225020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:57.325608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:58.314837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:59.397629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:00.363018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:10:07.413975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드행정동 명취약자 수취약자 백분위 수미세먼지 지수 값미세먼지 백분위 수강수량 지수 값강수량 백분위 수
행정동 코드1.0001.0000.5560.4980.3960.6820.3960.621
행정동 명1.0001.0001.0001.0001.0001.0001.0001.000
취약자 수0.5561.0001.0000.9680.4700.5450.4700.396
취약자 백분위 수0.4981.0000.9681.0000.6140.6630.6140.498
미세먼지 지수 값0.3961.0000.4700.6141.0000.9451.0000.923
미세먼지 백분위 수0.6821.0000.5450.6630.9451.0000.9450.945
강수량 지수 값0.3961.0000.4700.6141.0000.9451.0000.923
강수량 백분위 수0.6211.0000.3960.4980.9230.9450.9231.000
2023-12-10T22:10:07.659867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드취약자 수취약자 백분위 수미세먼지 지수 값미세먼지 백분위 수강수량 지수 값강수량 백분위 수
행정동 코드1.0000.7170.714-0.399-0.390-0.726-0.744
취약자 수0.7171.0001.000-0.382-0.379-0.547-0.535
취약자 백분위 수0.7141.0001.000-0.385-0.382-0.545-0.533
미세먼지 지수 값-0.399-0.382-0.3851.0000.9970.6530.665
미세먼지 백분위 수-0.390-0.379-0.3820.9971.0000.6460.656
강수량 지수 값-0.726-0.547-0.5450.6530.6461.0000.963
강수량 백분위 수-0.744-0.535-0.5330.6650.6560.9631.000

Missing values

2023-12-10T22:10:01.432077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:10:01.631063image/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
01101053사직동256545394.711841.81784
11101054삼청동8389230.248631.0663
21101055부암동266947106.311500.4551
31101056평창동50707273.418430.29845
41101057무악동23864332.893130.15135
51101058교남동124422197.355600.90860
61101060가회동125822131.57530.60654
71101061종로1·2·3·4가동177334953.8851004.391100
81101063종로5·6가동125522159.121550.74757
91101064이화동17963496.007470.44951
행정동 코드행정동 명취약자 수취약자 백분위 수미세먼지 지수 값미세먼지 백분위 수강수량 지수 값강수량 백분위 수
901106088장안2동83189134.884220.14826
911106089이문1동66268441.944340.14827
921106090이문2동58277841.944340.14821
931106091답십리1동62678231.55770.14933
941107052면목2동61768134.884220.14818
951107054면목4동52167334.884220.14827
961107055면목5동27194734.884220.14816
971107057면목7동55207634.884220.14823
981107059상봉1동61768141.944340.14819
991107060상봉2동37645941.944340.14821