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

행정동코드 is highly overall correlated with 취약자수 and 1 other fieldsHigh correlation
취약자수 is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
취약자백분위 is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
미세먼지지수 is highly overall correlated with 미세먼지백분위 and 2 other fieldsHigh correlation
미세먼지백분위 is highly overall correlated with 미세먼지지수 and 2 other fieldsHigh correlation
강수량지수 is highly overall correlated with 미세먼지지수 and 2 other fieldsHigh correlation
강수량백분위 is highly overall correlated with 미세먼지지수 and 2 other fieldsHigh correlation
행정동코드 has unique valuesUnique
행정동명 has unique valuesUnique

Reproduction

Analysis started2024-04-21 15:11:17.973021
Analysis finished2024-04-21 15:11:28.986407
Duration11.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동코드
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
2024-04-22T00:11:29.185134image/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
2024-04-22T00:11:29.629127image/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%

행정동명
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
2024-04-22T00:11:30.820376image/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%
2024-04-22T00:11:32.289089image/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%

취약자수
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3994.46
Minimum437
Maximum9731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-22T00:11:32.708094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437
5-th percentile1096.45
Q12423
median3566.5
Q35441.75
95-th percentile7744.05
Maximum9731
Range9294
Interquartile range (IQR)3018.75

Descriptive statistics

Standard deviation2196.0747
Coefficient of variation (CV)0.54978011
Kurtosis-0.18418981
Mean3994.46
Median Absolute Deviation (MAD)1485.5
Skewness0.62773066
Sum399446
Variance4822743.9
MonotonicityNot monotonic
2024-04-22T00:11:33.056371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9731 2
 
2.0%
5907 1
 
1.0%
7003 1
 
1.0%
4398 1
 
1.0%
2330 1
 
1.0%
6946 1
 
1.0%
3819 1
 
1.0%
4917 1
 
1.0%
3427 1
 
1.0%
4337 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
437 1
1.0%
460 1
1.0%
743 1
1.0%
763 1
1.0%
1010 1
1.0%
1101 1
1.0%
1145 1
1.0%
1167 1
1.0%
1283 1
1.0%
1332 1
1.0%
ValueCountFrequency (%)
9731 2
2.0%
9637 1
1.0%
8191 1
1.0%
7878 1
1.0%
7737 1
1.0%
7523 1
1.0%
7496 1
1.0%
7434 1
1.0%
7055 1
1.0%
7004 1
1.0%

취약자백분위
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.53
Minimum2
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-22T00:11:33.298150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile16.85
Q141
median54.5
Q372
95-th percentile87
Maximum93
Range91
Interquartile range (IQR)31

Descriptive statistics

Standard deviation22.223136
Coefficient of variation (CV)0.40753963
Kurtosis-0.47458834
Mean54.53
Median Absolute Deviation (MAD)15
Skewness-0.29619167
Sum5453
Variance493.86778
MonotonicityNot monotonic
2024-04-22T00:11:33.591603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 4
 
4.0%
69 4
 
4.0%
42 4
 
4.0%
55 4
 
4.0%
79 3
 
3.0%
61 3
 
3.0%
49 3
 
3.0%
52 3
 
3.0%
32 3
 
3.0%
53 3
 
3.0%
Other values (50) 66
66.0%
ValueCountFrequency (%)
2 2
2.0%
6 1
1.0%
7 1
1.0%
14 1
1.0%
17 1
1.0%
18 1
1.0%
19 1
1.0%
22 1
1.0%
23 1
1.0%
25 1
1.0%
ValueCountFrequency (%)
93 3
3.0%
88 1
 
1.0%
87 2
2.0%
86 2
2.0%
85 1
 
1.0%
84 1
 
1.0%
83 3
3.0%
82 1
 
1.0%
81 1
 
1.0%
79 3
3.0%

미세먼지지수
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.02615
Minimum11.848
Maximum343.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-22T00:11:33.961259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.848
5-th percentile12.345
Q114.146
median14.731
Q337.855
95-th percentile129.98975
Maximum343.59
Range331.742
Interquartile range (IQR)23.709

Descriptive statistics

Standard deviation51.502825
Coefficient of variation (CV)1.3544055
Kurtosis15.688251
Mean38.02615
Median Absolute Deviation (MAD)1.664
Skewness3.5304539
Sum3802.615
Variance2652.541
MonotonicityNot monotonic
2024-04-22T00:11:34.362913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
14.146 22
22.0%
14.243 12
12.0%
14.731 12
12.0%
12.345 9
 
9.0%
18.927 7
 
7.0%
15.673 4
 
4.0%
75.71 4
 
4.0%
142.175 2
 
2.0%
118.479 2
 
2.0%
94.637 2
 
2.0%
Other values (20) 24
24.0%
ValueCountFrequency (%)
11.848 1
 
1.0%
12.345 9
9.0%
14.146 22
22.0%
14.243 12
12.0%
14.731 12
12.0%
15.673 4
 
4.0%
18.927 7
 
7.0%
24.689 1
 
1.0%
24.822 1
 
1.0%
28.293 2
 
2.0%
ValueCountFrequency (%)
343.59 1
 
1.0%
272.502 1
 
1.0%
142.175 2
2.0%
130.327 1
 
1.0%
129.972 1
 
1.0%
123.076 1
 
1.0%
118.479 2
2.0%
94.637 2
2.0%
82.935 1
 
1.0%
75.71 4
4.0%

미세먼지백분위
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.92
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-22T00:11:34.758223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q17
median10.5
Q345
95-th percentile64
Maximum99
Range98
Interquartile range (IQR)38

Descriptive statistics

Standard deviation23.664609
Coefficient of variation (CV)0.94962317
Kurtosis-0.0070489725
Mean24.92
Median Absolute Deviation (MAD)7.5
Skewness0.9563509
Sum2492
Variance560.01374
MonotonicityNot monotonic
2024-04-22T00:11:35.156680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
7 23
23.0%
8 7
 
7.0%
34 7
 
7.0%
10 6
 
6.0%
11 6
 
6.0%
1 5
 
5.0%
2 5
 
5.0%
55 4
 
4.0%
17 4
 
4.0%
6 4
 
4.0%
Other values (16) 29
29.0%
ValueCountFrequency (%)
1 5
 
5.0%
2 5
 
5.0%
6 4
 
4.0%
7 23
23.0%
8 7
 
7.0%
10 6
 
6.0%
11 6
 
6.0%
17 4
 
4.0%
34 7
 
7.0%
36 2
 
2.0%
ValueCountFrequency (%)
99 1
 
1.0%
95 1
 
1.0%
68 2
2.0%
64 2
2.0%
63 3
3.0%
58 2
2.0%
57 1
 
1.0%
55 4
4.0%
54 3
3.0%
51 1
 
1.0%

강수량지수
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87225
Minimum0.294
Maximum8.517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-22T00:11:35.531088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.294
5-th percentile0.319
Q10.327
median0.339
Q30.92175
95-th percentile3.11225
Maximum8.517
Range8.223
Interquartile range (IQR)0.59475

Descriptive statistics

Standard deviation1.2564245
Coefficient of variation (CV)1.4404408
Kurtosis17.729086
Mean0.87225
Median Absolute Deviation (MAD)0.012
Skewness3.8100224
Sum87.225
Variance1.5786026
MonotonicityNot monotonic
2024-04-22T00:11:35.915694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.339 28
28.0%
0.327 22
22.0%
0.333 9
 
9.0%
0.319 7
 
7.0%
1.274 4
 
4.0%
3.524 2
 
2.0%
1.762 2
 
2.0%
1.175 2
 
2.0%
0.655 2
 
2.0%
2.937 2
 
2.0%
Other values (18) 20
20.0%
ValueCountFrequency (%)
0.294 1
 
1.0%
0.319 7
 
7.0%
0.327 22
22.0%
0.333 9
 
9.0%
0.339 28
28.0%
0.637 2
 
2.0%
0.648 1
 
1.0%
0.655 2
 
2.0%
0.666 1
 
1.0%
0.667 1
 
1.0%
ValueCountFrequency (%)
8.517 1
1.0%
6.755 1
1.0%
3.524 2
2.0%
3.231 1
1.0%
3.106 1
1.0%
3.005 1
1.0%
2.937 2
2.0%
2.056 1
1.0%
1.762 2
2.0%
1.604 1
1.0%

강수량백분위
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.48
Minimum3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-22T00:11:36.288083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q18
median18
Q346
95-th percentile72
Maximum100
Range97
Interquartile range (IQR)38

Descriptive statistics

Standard deviation23.201132
Coefficient of variation (CV)0.87617568
Kurtosis0.59768626
Mean26.48
Median Absolute Deviation (MAD)10
Skewness1.2031585
Sum2648
Variance538.29253
MonotonicityNot monotonic
2024-04-22T00:11:36.664792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
18 23
23.0%
8 15
15.0%
7 7
 
7.0%
6 7
 
7.0%
9 6
 
6.0%
17 5
 
5.0%
52 4
 
4.0%
55 3
 
3.0%
10 3
 
3.0%
51 3
 
3.0%
Other values (13) 24
24.0%
ValueCountFrequency (%)
3 1
 
1.0%
6 7
 
7.0%
7 7
 
7.0%
8 15
15.0%
9 6
 
6.0%
10 3
 
3.0%
17 5
 
5.0%
18 23
23.0%
37 2
 
2.0%
38 3
 
3.0%
ValueCountFrequency (%)
100 1
 
1.0%
98 1
 
1.0%
77 2
2.0%
72 2
2.0%
68 3
3.0%
60 1
 
1.0%
57 2
2.0%
55 3
3.0%
52 4
4.0%
51 3
3.0%

Interactions

2024-04-22T00:11:26.435561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:18.359259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:20.080764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:21.751934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:23.160194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:24.244913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:25.321817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:26.690552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:18.614648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:20.333064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:22.000566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:23.317922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:24.435964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:25.475111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:26.927818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:18.856969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:20.566584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:22.232554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:23.501692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:24.576831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:25.612986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:27.160707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:19.095773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:20.795170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:22.459041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:23.648044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:24.714642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:25.742177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:27.411395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:19.352111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:21.046287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:22.708120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:23.811156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:24.870087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:25.938061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:27.650712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:19.598170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:21.282734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:22.895324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:23.957026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:25.017075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:26.076210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:28.075282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:19.834886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:21.515535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:23.023013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:24.099648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:25.173776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T00:11:26.204056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-22T00:11:36.913339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드행정동명취약자수취약자백분위미세먼지지수미세먼지백분위강수량지수강수량백분위
행정동코드1.0001.0000.4340.4720.3760.5470.3960.770
행정동명1.0001.0001.0001.0001.0001.0001.0001.000
취약자수0.4341.0001.0000.9680.1900.2460.2690.395
취약자백분위0.4721.0000.9681.0000.5300.4400.5900.471
미세먼지지수0.3761.0000.1900.5301.0000.8700.9970.881
미세먼지백분위0.5471.0000.2460.4400.8701.0000.8680.927
강수량지수0.3961.0000.2690.5900.9970.8681.0000.878
강수량백분위0.7701.0000.3950.4710.8810.9270.8781.000
2024-04-22T00:11:37.212609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드취약자수취약자백분위미세먼지지수미세먼지백분위강수량지수강수량백분위
행정동코드1.0000.7200.721-0.363-0.372-0.221-0.205
취약자수0.7201.0001.000-0.362-0.370-0.253-0.232
취약자백분위0.7211.0001.000-0.365-0.372-0.254-0.234
미세먼지지수-0.363-0.362-0.3651.0000.9900.7830.777
미세먼지백분위-0.372-0.370-0.3720.9901.0000.7630.757
강수량지수-0.221-0.253-0.2540.7830.7631.0000.991
강수량백분위-0.205-0.232-0.2340.7770.7570.9911.000

Missing values

2024-04-22T00:11:28.407954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-22T00:11:28.826166image/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

행정동코드행정동명취약자수취약자백분위미세먼지지수미세먼지백분위강수량지수강수량백분위
01101053사직동241741142.175683.52477
11101054삼청동743682.935572.05660
21101055부암동26384436.67440.94246
31101056평창동50646924.822360.64838
41101057무악동24714211.84810.2943
51101058교남동27844671.088541.76257
61101060가회동11671947.392481.17551
71101061종로1·2·3·4가동167631343.59998.517100
81101063종로5·6가동11451868.434541.60455
91101064이화동16483040.141460.94946
행정동코드행정동명취약자수취약자백분위미세먼지지수미세먼지백분위강수량지수강수량백분위
901106088장안2동96379314.24380.33918
911106089이문1동42446115.673170.33918
921106090이문2동58097515.673170.33918
931106091답십리1동74968614.14670.3278
941107052면목2동62797914.24380.33918
951107054면목4동50616914.24370.33918
961107055면목5동36805514.24380.33918
971107057면목7동57077414.24370.33918
981107059상봉1동62097815.673170.33918
991107060상봉2동39995815.673170.33918