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 13:10:09.119406
Analysis finished2023-12-10 13:10:17.844554
Duration8.73 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
2023-12-10T22:10:17.961262image/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
2023-12-10T22:10:18.192034image/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
2023-12-10T22:10:18.637644image/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%
2023-12-10T22:10:19.390995image/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
2023-12-10T22:10:19.622162image/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
2023-12-10T22:10:19.859426image/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
2023-12-10T22:10:20.163463image/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
2023-12-10T22:10:20.400498image/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%
Mean119.53609
Minimum15.238
Maximum1279.296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:10:20.734596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.238
5-th percentile37.798
Q137.798
median40.2375
Q3114.82075
95-th percentile443.34265
Maximum1279.296
Range1264.058
Interquartile range (IQR)77.02275

Descriptive statistics

Standard deviation190.52403
Coefficient of variation (CV)1.593862
Kurtosis17.828274
Mean119.53609
Median Absolute Deviation (MAD)2.4395
Skewness3.8233723
Sum11953.609
Variance36299.407
MonotonicityNot monotonic
2023-12-10T22:10:21.014365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
37.798 22
22.0%
38.961 12
12.0%
39.111 12
12.0%
41.364 9
 
9.0%
47.243 7
 
7.0%
15.238 4
 
4.0%
188.973 4
 
4.0%
529.364 2
 
2.0%
441.137 2
 
2.0%
236.216 2
 
2.0%
Other values (20) 24
24.0%
ValueCountFrequency (%)
15.238 4
 
4.0%
37.798 22
22.0%
38.961 12
12.0%
39.111 12
12.0%
41.364 9
9.0%
44.114 1
 
1.0%
47.243 7
 
7.0%
74.984 1
 
1.0%
75.597 2
 
2.0%
76.91 1
 
1.0%
ValueCountFrequency (%)
1279.296 1
1.0%
1014.614 1
1.0%
529.364 2
2.0%
485.25 1
1.0%
441.137 2
2.0%
428.506 1
1.0%
409.561 1
1.0%
308.796 1
1.0%
264.682 2
2.0%
236.216 2
2.0%

미세먼지 백분위 수
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.8
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:10:21.246714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q119
median27
Q349.25
95-th percentile84.2
Maximum100
Range99
Interquartile range (IQR)30.25

Descriptive statistics

Standard deviation22.496689
Coefficient of variation (CV)0.62839913
Kurtosis0.81929588
Mean35.8
Median Absolute Deviation (MAD)9
Skewness1.163543
Sum3580
Variance506.10101
MonotonicityNot monotonic
2023-12-10T22:10:21.483977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
23 23
23.0%
18 14
14.0%
30 9
 
9.0%
19 8
 
8.0%
35 7
 
7.0%
57 5
 
5.0%
1 4
 
4.0%
42 3
 
3.0%
84 3
 
3.0%
46 2
 
2.0%
Other values (16) 22
22.0%
ValueCountFrequency (%)
1 4
 
4.0%
18 14
14.0%
19 8
 
8.0%
23 23
23.0%
24 1
 
1.0%
30 9
 
9.0%
34 1
 
1.0%
35 7
 
7.0%
41 1
 
1.0%
42 3
 
3.0%
ValueCountFrequency (%)
100 2
 
2.0%
91 2
 
2.0%
88 1
 
1.0%
84 3
3.0%
81 1
 
1.0%
68 1
 
1.0%
63 2
 
2.0%
60 2
 
2.0%
57 5
5.0%
56 2
 
2.0%

강수량 지수 값
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.65725
Minimum0.401
Maximum51.391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:10:21.775927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.401
5-th percentile0.401
Q10.401
median0.746
Q32.42325
95-th percentile17.8096
Maximum51.391
Range50.99
Interquartile range (IQR)2.02225

Descriptive statistics

Standard deviation7.7880883
Coefficient of variation (CV)2.129493
Kurtosis18.68068
Mean3.65725
Median Absolute Deviation (MAD)0.345
Skewness3.9751964
Sum365.725
Variance60.65432
MonotonicityNot monotonic
2023-12-10T22:10:22.056478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.401 28
28.0%
0.746 22
22.0%
0.566 9
 
9.0%
1.015 7
 
7.0%
4.059 4
 
4.0%
21.265 2
 
2.0%
10.633 2
 
2.0%
7.088 2
 
2.0%
1.491 2
 
2.0%
17.721 2
 
2.0%
Other values (18) 20
20.0%
ValueCountFrequency (%)
0.401 28
28.0%
0.566 9
 
9.0%
0.746 22
22.0%
1.015 7
 
7.0%
1.131 1
 
1.0%
1.147 1
 
1.0%
1.491 2
 
2.0%
1.698 1
 
1.0%
1.772 1
 
1.0%
2.029 2
 
2.0%
ValueCountFrequency (%)
51.391 1
1.0%
40.759 1
1.0%
21.265 2
2.0%
19.493 1
1.0%
17.721 2
2.0%
15.668 1
1.0%
12.588 1
1.0%
12.405 1
1.0%
10.633 2
2.0%
7.088 2
2.0%

강수량 백분위 수
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.91
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:10:22.250872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11.95
Q123
median37
Q358.25
95-th percentile100
Maximum100
Range90
Interquartile range (IQR)35.25

Descriptive statistics

Standard deviation27.784923
Coefficient of variation (CV)0.60520415
Kurtosis-0.4695756
Mean45.91
Median Absolute Deviation (MAD)16
Skewness0.82044824
Sum4591
Variance772.00192
MonotonicityNot monotonic
2023-12-10T22:10:22.437767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
37 19
19.0%
100 12
 
12.0%
33 9
 
9.0%
45 7
 
7.0%
23 5
 
5.0%
17 4
 
4.0%
75 4
 
4.0%
36 3
 
3.0%
16 3
 
3.0%
10 3
 
3.0%
Other values (21) 31
31.0%
ValueCountFrequency (%)
10 3
3.0%
11 2
2.0%
12 2
2.0%
16 3
3.0%
17 4
4.0%
18 2
2.0%
19 2
2.0%
20 2
2.0%
21 1
 
1.0%
22 1
 
1.0%
ValueCountFrequency (%)
100 12
12.0%
95 2
 
2.0%
84 2
 
2.0%
80 1
 
1.0%
76 1
 
1.0%
75 4
 
4.0%
67 1
 
1.0%
66 1
 
1.0%
62 1
 
1.0%
57 1
 
1.0%

Interactions

2023-12-10T22:10:16.529406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:09.534638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:10.826802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:11.792765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:12.799580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:13.956931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:15.516800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:16.668143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:09.667187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:10.967546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:11.944345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:12.934511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:14.108432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:15.714513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:16.808106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:09.792893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:11.093234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:12.133549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:13.063457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:14.244723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:15.837384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:16.935719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:09.977469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:11.227529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:12.274778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:13.196640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:14.387966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:15.970907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:17.061601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:10.167747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:11.366379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:12.402612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:13.449216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:14.612765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:16.162531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:17.188910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:10.424707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:11.506051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:12.540464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:13.676584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:14.826790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:16.285287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:17.307059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:10.591632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:11.624234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:12.669036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:13.821666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:15.380802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:16.417033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:10:22.621947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드행정동 명취약자 수취약자 백분위 수미세먼지 지수 값미세먼지 백분위 수강수량 지수 값강수량 백분위 수
행정동 코드1.0001.0000.5560.4980.3270.6930.1240.708
행정동 명1.0001.0001.0001.0001.0001.0001.0001.000
취약자 수0.5561.0001.0000.9680.4540.4080.3330.514
취약자 백분위 수0.4981.0000.9681.0000.6070.5220.6160.551
미세먼지 지수 값0.3271.0000.4540.6071.0000.8650.9870.625
미세먼지 백분위 수0.6931.0000.4080.5220.8651.0000.8100.871
강수량 지수 값0.1241.0000.3330.6160.9870.8101.0000.513
강수량 백분위 수0.7081.0000.5140.5510.6250.8710.5131.000
2023-12-10T22:10:22.854969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드취약자 수취약자 백분위 수미세먼지 지수 값미세먼지 백분위 수강수량 지수 값강수량 백분위 수
행정동 코드1.0000.7170.714-0.538-0.539-0.760-0.747
취약자 수0.7171.0001.000-0.453-0.462-0.567-0.574
취약자 백분위 수0.7141.0001.000-0.453-0.463-0.565-0.571
미세먼지 지수 값-0.538-0.453-0.4531.0000.9910.7300.727
미세먼지 백분위 수-0.539-0.462-0.4630.9911.0000.7300.729
강수량 지수 값-0.760-0.567-0.5650.7300.7301.0000.986
강수량 백분위 수-0.747-0.574-0.5710.7270.7290.9861.000

Missing values

2023-12-10T22:10:17.479532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:10:17.751051image/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
01101053사직동256545529.3649121.265100
11101054삼청동8389308.7966812.405100
21101055부암동266947119.098503.4767
31101056평창동50707274.984411.69853
41101057무악동23864344.114341.77254
51101058교남동124422264.6826310.633100
61101060가회동125822176.455567.08895
71101061종로1·2·3·4가동1773341279.29610051.391100
81101063종로5·6가동125522195.308574.75480
91101064이화동179634119.711503.26366
행정동 코드행정동 명취약자 수취약자 백분위 수미세먼지 지수 값미세먼지 백분위 수강수량 지수 값강수량 백분위 수
901106088장안2동83189138.961230.40119
911106089이문1동66268415.23810.40112
921106090이문2동58277815.23810.40118
931106091답십리1동62678237.798190.74637
941107052면목2동61768138.961230.40118
951107054면목4동52167338.961230.40123
961107055면목5동27194738.961230.40116
971107057면목7동55207638.961230.40125
981107059상봉1동61768115.23810.40111
991107060상봉2동37645915.23810.40117