Overview

Dataset statistics

Number of variables17
Number of observations44
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 KiB
Average record size in memory155.0 B

Variable types

Text1
Categorical3
Numeric13

Dataset

Description인천광역시 부평구 장애 유형별 등록현황 데이터는 각 동별 지체, 시각, 청각, 언어, 지적 등 장애 유형별 등록 현황을 제공합니다.
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15112955&srcSe=7661IVAWM27C61E190

Alerts

지체 is highly overall correlated with 시각 and 10 other fieldsHigh correlation
시각 is highly overall correlated with 지체 and 10 other fieldsHigh correlation
청각 is highly overall correlated with 지체 and 10 other fieldsHigh correlation
언어 is highly overall correlated with 뇌병변High correlation
지적 is highly overall correlated with 지체 and 11 other fieldsHigh correlation
뇌병변 is highly overall correlated with 언어 and 5 other fieldsHigh correlation
자폐성 is highly overall correlated with 지체 and 10 other fieldsHigh correlation
정신 is highly overall correlated with 지체 and 11 other fieldsHigh correlation
신장 is highly overall correlated with 지체 and 10 other fieldsHigh correlation
호흡기 is highly overall correlated with 지체 and 11 other fieldsHigh correlation
is highly overall correlated with 지체 and 10 other fieldsHigh correlation
장루_요루 is highly overall correlated with 지체 and 9 other fieldsHigh correlation
뇌전증 is highly overall correlated with 지체 and 3 other fieldsHigh correlation
장애구분(1 심한장애 2 심하지 않은 장애) is highly overall correlated with 지체 and 9 other fieldsHigh correlation
심장 is highly overall correlated with 지적 and 1 other fieldsHigh correlation
안면 is highly overall correlated with 정신High correlation
언어 has 2 (4.5%) zerosZeros
지적 has 22 (50.0%) zerosZeros
자폐성 has 22 (50.0%) zerosZeros
정신 has 15 (34.1%) zerosZeros
호흡기 has 21 (47.7%) zerosZeros
has 16 (36.4%) zerosZeros
장루_요루 has 14 (31.8%) zerosZeros
뇌전증 has 14 (31.8%) zerosZeros

Reproduction

Analysis started2024-05-10 22:04:43.645899
Analysis finished2024-05-10 22:05:30.523831
Duration46.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct22
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-05-10T22:05:30.763768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9545455
Min length3

Characters and Unicode

Total characters174
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부평1동
2nd row부평2동
3rd row부평3동
4th row부평4동
5th row부평5동
ValueCountFrequency (%)
부평1동 2
 
4.5%
부평2동 2
 
4.5%
십정1동 2
 
4.5%
일신동 2
 
4.5%
부개3동 2
 
4.5%
부개2동 2
 
4.5%
부개1동 2
 
4.5%
삼산2동 2
 
4.5%
삼산1동 2
 
4.5%
갈산2동 2
 
4.5%
Other values (12) 24
54.5%
2024-05-10T22:05:31.648939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44
25.3%
18
10.3%
16
 
9.2%
1 14
 
8.0%
2 14
 
8.0%
12
 
6.9%
8
 
4.6%
3 6
 
3.4%
6
 
3.4%
4 4
 
2.3%
Other values (10) 32
18.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 132
75.9%
Decimal Number 42
 
24.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
44
33.3%
18
13.6%
16
 
12.1%
12
 
9.1%
8
 
6.1%
6
 
4.5%
4
 
3.0%
4
 
3.0%
4
 
3.0%
4
 
3.0%
Other values (4) 12
 
9.1%
Decimal Number
ValueCountFrequency (%)
1 14
33.3%
2 14
33.3%
3 6
14.3%
4 4
 
9.5%
5 2
 
4.8%
6 2
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 132
75.9%
Common 42
 
24.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
44
33.3%
18
13.6%
16
 
12.1%
12
 
9.1%
8
 
6.1%
6
 
4.5%
4
 
3.0%
4
 
3.0%
4
 
3.0%
4
 
3.0%
Other values (4) 12
 
9.1%
Common
ValueCountFrequency (%)
1 14
33.3%
2 14
33.3%
3 6
14.3%
4 4
 
9.5%
5 2
 
4.8%
6 2
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 132
75.9%
ASCII 42
 
24.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
44
33.3%
18
13.6%
16
 
12.1%
12
 
9.1%
8
 
6.1%
6
 
4.5%
4
 
3.0%
4
 
3.0%
4
 
3.0%
4
 
3.0%
Other values (4) 12
 
9.1%
ASCII
ValueCountFrequency (%)
1 14
33.3%
2 14
33.3%
3 6
14.3%
4 4
 
9.5%
5 2
 
4.8%
6 2
 
4.8%
Distinct2
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
1
22 
2
22 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 22
50.0%
2 22
50.0%

Length

2024-05-10T22:05:32.029317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:05:32.400100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 22
50.0%
2 22
50.0%

지체
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.56818
Minimum36
Maximum713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-10T22:05:32.820189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile66.6
Q1111.75
median223
Q3428.5
95-th percentile601.9
Maximum713
Range677
Interquartile range (IQR)316.75

Descriptive statistics

Standard deviation187.11287
Coefficient of variation (CV)0.6967053
Kurtosis-0.77516859
Mean268.56818
Median Absolute Deviation (MAD)132
Skewness0.60524736
Sum11817
Variance35011.228
MonotonicityNot monotonic
2024-05-10T22:05:33.246578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
70 2
 
4.5%
115 2
 
4.5%
336 1
 
2.3%
511 1
 
2.3%
334 1
 
2.3%
259 1
 
2.3%
430 1
 
2.3%
428 1
 
2.3%
255 1
 
2.3%
191 1
 
2.3%
Other values (32) 32
72.7%
ValueCountFrequency (%)
36 1
2.3%
46 1
2.3%
66 1
2.3%
70 2
4.5%
73 1
2.3%
81 1
2.3%
83 1
2.3%
99 1
2.3%
103 1
2.3%
108 1
2.3%
ValueCountFrequency (%)
713 1
2.3%
634 1
2.3%
610 1
2.3%
556 1
2.3%
526 1
2.3%
511 1
2.3%
476 1
2.3%
460 1
2.3%
451 1
2.3%
438 1
2.3%

시각
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.272727
Minimum9
Maximum166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-10T22:05:33.704435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile10
Q120.5
median51
Q390.5
95-th percentile134.7
Maximum166
Range157
Interquartile range (IQR)70

Descriptive statistics

Standard deviation43.46416
Coefficient of variation (CV)0.74587482
Kurtosis-0.50761901
Mean58.272727
Median Absolute Deviation (MAD)34.5
Skewness0.72034292
Sum2564
Variance1889.1332
MonotonicityNot monotonic
2024-05-10T22:05:34.130378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
28 2
 
4.5%
10 2
 
4.5%
9 2
 
4.5%
53 2
 
4.5%
92 2
 
4.5%
90 2
 
4.5%
105 1
 
2.3%
79 1
 
2.3%
49 1
 
2.3%
133 1
 
2.3%
Other values (28) 28
63.6%
ValueCountFrequency (%)
9 2
4.5%
10 2
4.5%
12 1
2.3%
13 1
2.3%
14 1
2.3%
15 1
2.3%
16 1
2.3%
17 1
2.3%
19 1
2.3%
21 1
2.3%
ValueCountFrequency (%)
166 1
2.3%
143 1
2.3%
135 1
2.3%
133 1
2.3%
128 1
2.3%
122 1
2.3%
105 1
2.3%
100 1
2.3%
94 1
2.3%
92 2
4.5%

청각
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.15909
Minimum11
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-10T22:05:34.507100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile22.3
Q146.25
median84.5
Q3184.25
95-th percentile262
Maximum365
Range354
Interquartile range (IQR)138

Descriptive statistics

Standard deviation85.110036
Coefficient of variation (CV)0.75212725
Kurtosis0.25915758
Mean113.15909
Median Absolute Deviation (MAD)53
Skewness0.90786343
Sum4979
Variance7243.7183
MonotonicityNot monotonic
2024-05-10T22:05:35.027195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
59 2
 
4.5%
47 2
 
4.5%
72 1
 
2.3%
183 1
 
2.3%
142 1
 
2.3%
100 1
 
2.3%
196 1
 
2.3%
226 1
 
2.3%
131 1
 
2.3%
49 1
 
2.3%
Other values (32) 32
72.7%
ValueCountFrequency (%)
11 1
2.3%
15 1
2.3%
22 1
2.3%
24 1
2.3%
26 1
2.3%
27 1
2.3%
39 1
2.3%
40 1
2.3%
42 1
2.3%
43 1
2.3%
ValueCountFrequency (%)
365 1
2.3%
282 1
2.3%
268 1
2.3%
228 1
2.3%
226 1
2.3%
219 1
2.3%
201 1
2.3%
196 1
2.3%
192 1
2.3%
189 1
2.3%

언어
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9545455
Minimum0
Maximum19
Zeros2
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-10T22:05:35.407846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median7
Q39
95-th percentile14
Maximum19
Range19
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.1030592
Coefficient of variation (CV)0.58998237
Kurtosis0.48461896
Mean6.9545455
Median Absolute Deviation (MAD)3
Skewness0.64358745
Sum306
Variance16.835095
MonotonicityNot monotonic
2024-05-10T22:05:35.944397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
4 5
11.4%
7 5
11.4%
5 5
11.4%
2 5
11.4%
8 5
11.4%
12 3
6.8%
9 3
6.8%
14 3
6.8%
6 3
6.8%
10 2
 
4.5%
Other values (4) 5
11.4%
ValueCountFrequency (%)
0 2
 
4.5%
2 5
11.4%
3 1
 
2.3%
4 5
11.4%
5 5
11.4%
6 3
6.8%
7 5
11.4%
8 5
11.4%
9 3
6.8%
10 2
 
4.5%
ValueCountFrequency (%)
19 1
 
2.3%
14 3
6.8%
12 3
6.8%
11 1
 
2.3%
10 2
 
4.5%
9 3
6.8%
8 5
11.4%
7 5
11.4%
6 3
6.8%
5 5
11.4%

지적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.863636
Minimum0
Maximum179
Zeros22
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-10T22:05:36.326348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median18.5
Q392
95-th percentile123.55
Maximum179
Range179
Interquartile range (IQR)92

Descriptive statistics

Standard deviation53.189259
Coefficient of variation (CV)1.1112666
Kurtosis-1.0554123
Mean47.863636
Median Absolute Deviation (MAD)18.5
Skewness0.53331898
Sum2106
Variance2829.0973
MonotonicityNot monotonic
2024-05-10T22:05:36.760054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 22
50.0%
108 3
 
6.8%
72 2
 
4.5%
95 1
 
2.3%
138 1
 
2.3%
76 1
 
2.3%
64 1
 
2.3%
91 1
 
2.3%
74 1
 
2.3%
88 1
 
2.3%
Other values (10) 10
22.7%
ValueCountFrequency (%)
0 22
50.0%
37 1
 
2.3%
45 1
 
2.3%
64 1
 
2.3%
72 2
 
4.5%
74 1
 
2.3%
76 1
 
2.3%
82 1
 
2.3%
87 1
 
2.3%
88 1
 
2.3%
ValueCountFrequency (%)
179 1
 
2.3%
138 1
 
2.3%
124 1
 
2.3%
121 1
 
2.3%
118 1
 
2.3%
110 1
 
2.3%
109 1
 
2.3%
108 3
6.8%
95 1
 
2.3%
91 1
 
2.3%

뇌병변
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.840909
Minimum15
Maximum156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-10T22:05:37.119362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile26.3
Q138.75
median52.5
Q370.5
95-th percentile100.7
Maximum156
Range141
Interquartile range (IQR)31.75

Descriptive statistics

Standard deviation26.823231
Coefficient of variation (CV)0.47190011
Kurtosis3.1216402
Mean56.840909
Median Absolute Deviation (MAD)16.5
Skewness1.3629509
Sum2501
Variance719.48573
MonotonicityNot monotonic
2024-05-10T22:05:37.545964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
32 3
 
6.8%
39 2
 
4.5%
52 2
 
4.5%
61 2
 
4.5%
50 2
 
4.5%
43 2
 
4.5%
40 2
 
4.5%
73 2
 
4.5%
33 1
 
2.3%
26 1
 
2.3%
Other values (25) 25
56.8%
ValueCountFrequency (%)
15 1
 
2.3%
20 1
 
2.3%
26 1
 
2.3%
28 1
 
2.3%
31 1
 
2.3%
32 3
6.8%
33 1
 
2.3%
36 1
 
2.3%
38 1
 
2.3%
39 2
4.5%
ValueCountFrequency (%)
156 1
2.3%
108 1
2.3%
101 1
2.3%
99 1
2.3%
93 1
2.3%
88 1
2.3%
76 1
2.3%
74 1
2.3%
73 2
4.5%
72 1
2.3%

자폐성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4772727
Minimum0
Maximum29
Zeros22
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-10T22:05:37.960433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q314
95-th percentile24
Maximum29
Range29
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.0874015
Coefficient of variation (CV)1.2153364
Kurtosis-0.65312852
Mean7.4772727
Median Absolute Deviation (MAD)1.5
Skewness0.83473633
Sum329
Variance82.580867
MonotonicityNot monotonic
2024-05-10T22:05:38.378718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 22
50.0%
19 3
 
6.8%
13 2
 
4.5%
6 2
 
4.5%
14 2
 
4.5%
16 2
 
4.5%
12 2
 
4.5%
24 2
 
4.5%
5 1
 
2.3%
29 1
 
2.3%
Other values (5) 5
 
11.4%
ValueCountFrequency (%)
0 22
50.0%
3 1
 
2.3%
5 1
 
2.3%
6 2
 
4.5%
8 1
 
2.3%
9 1
 
2.3%
12 2
 
4.5%
13 2
 
4.5%
14 2
 
4.5%
16 2
 
4.5%
ValueCountFrequency (%)
29 1
 
2.3%
25 1
 
2.3%
24 2
4.5%
23 1
 
2.3%
19 3
6.8%
16 2
4.5%
14 2
4.5%
13 2
4.5%
12 2
4.5%
9 1
 
2.3%

정신
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.840909
Minimum0
Maximum168
Zeros15
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-10T22:05:38.873218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q338.25
95-th percentile62.65
Maximum168
Range168
Interquartile range (IQR)38.25

Descriptive statistics

Standard deviation30.883083
Coefficient of variation (CV)1.4140017
Kurtosis10.721662
Mean21.840909
Median Absolute Deviation (MAD)7
Skewness2.6713464
Sum961
Variance953.7648
MonotonicityNot monotonic
2024-05-10T22:05:39.351690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 15
34.1%
1 6
 
13.6%
42 2
 
4.5%
44 2
 
4.5%
23 2
 
4.5%
20 2
 
4.5%
168 1
 
2.3%
2 1
 
2.3%
46 1
 
2.3%
33 1
 
2.3%
Other values (11) 11
25.0%
ValueCountFrequency (%)
0 15
34.1%
1 6
 
13.6%
2 1
 
2.3%
12 1
 
2.3%
20 2
 
4.5%
23 2
 
4.5%
29 1
 
2.3%
30 1
 
2.3%
31 1
 
2.3%
33 1
 
2.3%
ValueCountFrequency (%)
168 1
2.3%
74 1
2.3%
64 1
2.3%
55 1
2.3%
46 1
2.3%
44 2
4.5%
42 2
4.5%
40 1
2.3%
39 1
2.3%
38 1
2.3%

신장
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.295455
Minimum2
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-10T22:05:39.891833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.15
Q111.75
median22
Q345.25
95-th percentile61.85
Maximum95
Range93
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation21.566101
Coefficient of variation (CV)0.76217547
Kurtosis0.48895267
Mean28.295455
Median Absolute Deviation (MAD)14.5
Skewness0.96073874
Sum1245
Variance465.09672
MonotonicityNot monotonic
2024-05-10T22:05:40.194645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
13 3
 
6.8%
14 3
 
6.8%
22 2
 
4.5%
15 2
 
4.5%
8 2
 
4.5%
5 2
 
4.5%
11 2
 
4.5%
6 2
 
4.5%
59 1
 
2.3%
17 1
 
2.3%
Other values (24) 24
54.5%
ValueCountFrequency (%)
2 1
 
2.3%
5 2
4.5%
6 2
4.5%
7 1
 
2.3%
8 2
4.5%
9 1
 
2.3%
11 2
4.5%
12 1
 
2.3%
13 3
6.8%
14 3
6.8%
ValueCountFrequency (%)
95 1
2.3%
67 1
2.3%
62 1
2.3%
61 1
2.3%
59 1
2.3%
57 1
2.3%
53 1
2.3%
50 1
2.3%
48 1
2.3%
47 1
2.3%

심장
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size484.0 B
0
21 
1
11 
2
3
 
2
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row2
2nd row3
3rd row0
4th row2
5th row1

Common Values

ValueCountFrequency (%)
0 21
47.7%
1 11
25.0%
2 9
20.5%
3 2
 
4.5%
4 1
 
2.3%

Length

2024-05-10T22:05:40.475776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:05:40.763564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21
47.7%
1 11
25.0%
2 9
20.5%
3 2
 
4.5%
4 1
 
2.3%

호흡기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5
Minimum0
Maximum12
Zeros21
Zeros (%)47.7%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-10T22:05:41.079068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile8.85
Maximum12
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2884576
Coefficient of variation (CV)1.315383
Kurtosis0.54018353
Mean2.5
Median Absolute Deviation (MAD)1
Skewness1.2105835
Sum110
Variance10.813953
MonotonicityNot monotonic
2024-05-10T22:05:41.438183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 21
47.7%
1 5
 
11.4%
4 5
 
11.4%
7 3
 
6.8%
6 2
 
4.5%
3 2
 
4.5%
5 1
 
2.3%
2 1
 
2.3%
10 1
 
2.3%
12 1
 
2.3%
Other values (2) 2
 
4.5%
ValueCountFrequency (%)
0 21
47.7%
1 5
 
11.4%
2 1
 
2.3%
3 2
 
4.5%
4 5
 
11.4%
5 1
 
2.3%
6 2
 
4.5%
7 3
 
6.8%
8 1
 
2.3%
9 1
 
2.3%
ValueCountFrequency (%)
12 1
 
2.3%
10 1
 
2.3%
9 1
 
2.3%
8 1
 
2.3%
7 3
6.8%
6 2
 
4.5%
5 1
 
2.3%
4 5
11.4%
3 2
 
4.5%
2 1
 
2.3%


Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0681818
Minimum0
Maximum14
Zeros16
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-10T22:05:41.772850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile10
Maximum14
Range14
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8723692
Coefficient of variation (CV)1.2621055
Kurtosis1.209915
Mean3.0681818
Median Absolute Deviation (MAD)1
Skewness1.3682588
Sum135
Variance14.995243
MonotonicityNot monotonic
2024-05-10T22:05:42.136682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16
36.4%
1 9
20.5%
3 4
 
9.1%
5 3
 
6.8%
14 2
 
4.5%
4 2
 
4.5%
10 2
 
4.5%
7 2
 
4.5%
6 2
 
4.5%
9 1
 
2.3%
ValueCountFrequency (%)
0 16
36.4%
1 9
20.5%
3 4
 
9.1%
4 2
 
4.5%
5 3
 
6.8%
6 2
 
4.5%
7 2
 
4.5%
8 1
 
2.3%
9 1
 
2.3%
10 2
 
4.5%
ValueCountFrequency (%)
14 2
 
4.5%
10 2
 
4.5%
9 1
 
2.3%
8 1
 
2.3%
7 2
 
4.5%
6 2
 
4.5%
5 3
 
6.8%
4 2
 
4.5%
3 4
9.1%
1 9
20.5%

안면
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size484.0 B
0
27 
1
15 
2
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)4.5%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 27
61.4%
1 15
34.1%
2 1
 
2.3%
4 1
 
2.3%

Length

2024-05-10T22:05:42.558565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T22:05:42.854467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
61.4%
1 15
34.1%
2 1
 
2.3%
4 1
 
2.3%

장루_요루
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2045455
Minimum0
Maximum13
Zeros14
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-10T22:05:43.126095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35.25
95-th percentile10.85
Maximum13
Range13
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation3.751462
Coefficient of variation (CV)1.170669
Kurtosis0.11971227
Mean3.2045455
Median Absolute Deviation (MAD)1
Skewness1.0674823
Sum141
Variance14.073467
MonotonicityNot monotonic
2024-05-10T22:05:43.391091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 14
31.8%
1 11
25.0%
7 4
 
9.1%
5 4
 
9.1%
4 3
 
6.8%
8 2
 
4.5%
2 1
 
2.3%
11 1
 
2.3%
13 1
 
2.3%
6 1
 
2.3%
Other values (2) 2
 
4.5%
ValueCountFrequency (%)
0 14
31.8%
1 11
25.0%
2 1
 
2.3%
4 3
 
6.8%
5 4
 
9.1%
6 1
 
2.3%
7 4
 
9.1%
8 2
 
4.5%
10 1
 
2.3%
11 1
 
2.3%
ValueCountFrequency (%)
13 1
 
2.3%
12 1
 
2.3%
11 1
 
2.3%
10 1
 
2.3%
8 2
4.5%
7 4
9.1%
6 1
 
2.3%
5 4
9.1%
4 3
6.8%
2 1
 
2.3%

뇌전증
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8636364
Minimum0
Maximum9
Zeros14
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-10T22:05:43.891031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0413278
Coefficient of variation (CV)1.0953466
Kurtosis2.5315753
Mean1.8636364
Median Absolute Deviation (MAD)1
Skewness1.4475824
Sum82
Variance4.167019
MonotonicityNot monotonic
2024-05-10T22:05:44.174210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 14
31.8%
1 11
25.0%
3 9
20.5%
2 3
 
6.8%
4 3
 
6.8%
5 2
 
4.5%
7 1
 
2.3%
9 1
 
2.3%
ValueCountFrequency (%)
0 14
31.8%
1 11
25.0%
2 3
 
6.8%
3 9
20.5%
4 3
 
6.8%
5 2
 
4.5%
7 1
 
2.3%
9 1
 
2.3%
ValueCountFrequency (%)
9 1
 
2.3%
7 1
 
2.3%
5 2
 
4.5%
4 3
 
6.8%
3 9
20.5%
2 3
 
6.8%
1 11
25.0%
0 14
31.8%

Interactions

2024-05-10T22:05:26.204063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:45.148793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:48.603957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:51.933217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:55.417420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:59.520192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:02.879720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:06.535037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:09.774935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:13.059816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:16.153245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:19.327119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:22.733393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:26.399334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:45.430056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:48.816151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:52.168910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:55.676950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:59.764334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:03.175823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:06.831081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:10.029449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:13.263985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:16.404651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:19.583810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:22.982638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:26.608434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:45.692413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:49.041474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:52.550731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:55.943950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:00.054840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:03.476757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:07.089470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:10.281874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:13.469294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:16.664303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:19.842057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:23.253694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:27.061512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:45.956660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:49.316576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:52.811072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:56.210839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:00.318015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:03.744649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:07.353124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:10.552773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:13.682441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:16.927680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:20.157960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:23.510357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:27.268470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:46.227590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:49.590688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:53.079369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:56.570415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:00.564434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:04.009874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:07.612560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:10.771870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:13.904256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:17.185685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:20.431385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:23.775510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:27.477502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:46.460531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:49.871725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:53.341215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:56.870050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:00.799278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:04.435233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:07.868718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:11.016715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:14.123844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:17.424337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:20.696542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:24.035654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:27.683840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:46.722073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:50.246829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:53.615403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:57.173194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:01.015337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:04.738006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:08.129547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:11.237148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:14.432551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:17.689582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:20.948205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:24.318959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:27.873225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:46.975650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:50.452339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:53.885133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:57.461517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:01.230630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:04.995885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:08.334122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:11.446929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:14.673946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:17.939688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:21.173595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:24.664762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:28.074070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:47.244522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:50.676776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:54.237858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:57.992296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:01.457556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:05.279886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:08.635579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:11.715611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:14.919476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:18.199275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:21.409658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:24.922056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:28.291918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:47.485030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:50.920674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:54.435879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:58.251602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:01.747721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:05.516175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:08.831288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:11.958816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:15.175977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:18.387058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:21.629180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:25.159789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:28.504179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:47.737804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:51.191133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:54.667826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:58.685722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:02.048536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:05.769429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:09.053262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:12.393821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:15.421195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:18.623908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:21.873839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:25.455071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:28.802885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:47.996827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:51.451523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:54.907819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:58.982081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:02.376695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:06.032528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:09.268560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:12.628188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:15.676188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:18.833813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:22.150278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:25.716040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:29.043940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:48.355488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:51.692649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:55.168848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:04:59.277984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:02.674374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:06.291847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:09.538591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:12.874355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:15.924386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:19.095723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:22.400592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T22:05:25.961077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T22:05:44.399805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동별구분장애구분(1 심한장애 2 심하지 않은 장애)지체시각청각언어지적뇌병변자폐성정신신장심장호흡기안면장루_요루뇌전증
동별구분1.0000.0000.0000.0000.0000.0000.0000.4950.0000.0000.0000.0000.0000.0000.3660.0000.677
장애구분(1 심한장애 2 심하지 않은 장애)0.0001.0000.9970.9620.8730.1461.0000.4911.0000.9970.9970.3100.9340.9690.0000.7730.524
지체0.0000.9971.0000.9060.8400.5980.3420.5180.0000.3660.6370.0000.0000.8280.0000.7600.708
시각0.0000.9620.9061.0000.8300.4520.0000.4980.0000.4780.6290.0000.0000.8300.0000.8100.674
청각0.0000.8730.8400.8301.0000.4720.4410.5090.0000.6450.5710.0000.0000.7770.0000.8750.737
언어0.0000.1460.5980.4520.4721.0000.0000.4910.0000.5020.2600.0000.0000.6020.0000.6560.408
지적0.0001.0000.3420.0000.4410.0001.0000.8200.8230.8250.9250.7020.8800.0000.0000.0000.405
뇌병변0.4950.4910.5180.4980.5090.4910.8201.0000.5640.7620.8310.6140.7390.0000.4130.0000.705
자폐성0.0001.0000.0000.0000.0000.0000.8230.5641.0000.7890.7980.6990.8660.0000.0000.0000.000
정신0.0000.9970.3660.4780.6450.5020.8250.7620.7891.0000.8270.5160.9020.0000.7050.0000.489
신장0.0000.9970.6370.6290.5710.2600.9250.8310.7980.8271.0000.5990.8320.2650.0000.0000.474
심장0.0000.3100.0000.0000.0000.0000.7020.6140.6990.5160.5991.0000.9360.0000.2090.3200.366
호흡기0.0000.9340.0000.0000.0000.0000.8800.7390.8660.9020.8320.9361.0000.0000.7220.0000.296
0.0000.9690.8280.8300.7770.6020.0000.0000.0000.0000.2650.0000.0001.0000.1070.7960.686
안면0.3660.0000.0000.0000.0000.0000.0000.4130.0000.7050.0000.2090.7220.1071.0000.0000.481
장루_요루0.0000.7730.7600.8100.8750.6560.0000.0000.0000.0000.0000.3200.0000.7960.0001.0000.505
뇌전증0.6770.5240.7080.6740.7370.4080.4050.7050.0000.4890.4740.3660.2960.6860.4810.5051.000
2024-05-10T22:05:44.838246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
심장안면장애구분(1 심한장애 2 심하지 않은 장애)
심장1.0000.1630.362
안면0.1631.0000.000
장애구분(1 심한장애 2 심하지 않은 장애)0.3620.0001.000
2024-05-10T22:05:45.140876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지체시각청각언어지적뇌병변자폐성정신신장호흡기장루_요루뇌전증장애구분(1 심한장애 2 심하지 않은 장애)심장안면
지체1.0000.9100.9230.331-0.684-0.012-0.680-0.629-0.515-0.6230.8110.7700.6290.8560.0000.000
시각0.9101.0000.9220.267-0.708-0.083-0.745-0.600-0.507-0.6130.8160.7880.5760.7490.0000.000
청각0.9230.9221.0000.294-0.688-0.050-0.695-0.583-0.506-0.6480.8190.7520.6070.8290.0000.000
언어0.3310.2670.2941.0000.2200.5980.1970.2680.3610.1330.1190.0900.3470.1160.0000.000
지적-0.684-0.708-0.6880.2201.0000.5960.9410.9090.8980.888-0.754-0.719-0.3200.9260.5060.000
뇌병변-0.012-0.083-0.0500.5980.5961.0000.5980.6300.7470.565-0.194-0.1640.0670.3370.4160.174
자폐성-0.680-0.745-0.6950.1970.9410.5981.0000.8620.8720.866-0.771-0.714-0.3930.9000.3320.000
정신-0.629-0.600-0.5830.2680.9090.6300.8621.0000.9120.810-0.674-0.646-0.2720.9030.3730.521
신장-0.515-0.507-0.5060.3610.8980.7470.8720.9121.0000.822-0.570-0.574-0.1970.8830.4020.000
호흡기-0.623-0.613-0.6480.1330.8880.5650.8660.8100.8221.000-0.727-0.639-0.3650.7030.6010.476
0.8110.8160.8190.119-0.754-0.194-0.771-0.674-0.570-0.7271.0000.7640.5250.7820.0000.000
장루_요루0.7700.7880.7520.090-0.719-0.164-0.714-0.646-0.574-0.6390.7641.0000.3980.7220.1680.000
뇌전증0.6290.5760.6070.347-0.3200.067-0.393-0.272-0.197-0.3650.5250.3981.0000.3610.2170.210
장애구분(1 심한장애 2 심하지 않은 장애)0.8560.7490.8290.1160.9260.3370.9000.9030.8830.7030.7820.7220.3611.0000.3620.000
심장0.0000.0000.0000.0000.5060.4160.3320.3730.4020.6010.0000.1680.2170.3621.0000.163
안면0.0000.0000.0000.0000.0000.1740.0000.5210.0000.4760.0000.0000.2100.0000.1631.000

Missing values

2024-05-10T22:05:29.424612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T22:05:30.105499image/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

동별구분장애구분(1 심한장애 2 심하지 않은 장애)지체시각청각언어지적뇌병변자폐성정신신장심장호흡기안면장루_요루뇌전증
0부평1동11402872410870194267261103
1부평2동170357578250134241311120
2부평3동17062274725264037060000
3부평4동1154415912118108146462270000
4부평5동11152959912472164457151111
5부평6동1831622512138123132140011
6산곡1동14610244454052326041010
7산곡2동11191247711073293038140000
8산곡3동199285178776163853040011
9산곡4동16613265723281222230100
동별구분장애구분(1 심한장애 2 심하지 않은 장애)지체시각청각언어지적뇌병변자폐성정신신장심장호흡기안면장루_요루뇌전증
34갈산1동2336681523031008101143
35갈산2동2460901838039018007084
36삼산1동271313336512099011520101129
37삼산2동239692127603900140014054
38부개1동2451921894050009107441
39부개2동23477914900320013004050
40부개3동247610518870480011016071
41일신동2272491055040005005151
42십정1동243810019280540014003073
43십정2동2556122282605802150060103