Overview

Dataset statistics

Number of variables17
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory150.7 B

Variable types

Text3
Categorical3
Numeric11

Dataset

Description경상남도_장애인 유형 및 장애정도별 등록현황 입니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15049403

Alerts

지체 is highly overall correlated with 시각 and 11 other fieldsHigh correlation
시각 is highly overall correlated with 지체 and 11 other fieldsHigh correlation
청각 is highly overall correlated with 지체 and 11 other fieldsHigh correlation
언어 is highly overall correlated with 지체 and 11 other fieldsHigh correlation
지적 is highly overall correlated with 지체 and 11 other fieldsHigh correlation
뇌병변 is highly overall correlated with 지체 and 11 other fieldsHigh correlation
정신 is highly overall correlated with 지체 and 11 other fieldsHigh correlation
신장 is highly overall correlated with 지체 and 11 other fieldsHigh correlation
호흡기 is highly overall correlated with 지체 and 11 other fieldsHigh correlation
장루.요루 is highly overall correlated with 지체 and 11 other fieldsHigh correlation
뇌전증 is highly overall correlated with 지체 and 11 other fieldsHigh correlation
심장 is highly overall correlated with 지체 and 10 other fieldsHigh correlation
안면 is highly overall correlated with 지체 and 10 other fieldsHigh correlation
지체 has unique valuesUnique
지적 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:57:54.788350
Analysis finished2023-12-11 00:58:06.779014
Duration11.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct18
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-11T09:58:06.879389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters108
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row창원시
2nd row창원시
3rd row진주시
4th row진주시
5th row통영시
ValueCountFrequency (%)
창원시 2
 
5.6%
진주시 2
 
5.6%
거창군 2
 
5.6%
함양군 2
 
5.6%
산청군 2
 
5.6%
하동군 2
 
5.6%
남해군 2
 
5.6%
고성군 2
 
5.6%
창년군 2
 
5.6%
함안군 2
 
5.6%
Other values (8) 16
44.4%
2023-12-11T09:58:07.143243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20
18.5%
16
14.8%
6
 
5.6%
6
 
5.6%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
2
 
1.9%
Other values (19) 38
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 108
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
20
18.5%
16
14.8%
6
 
5.6%
6
 
5.6%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
2
 
1.9%
Other values (19) 38
35.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 108
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
20
18.5%
16
14.8%
6
 
5.6%
6
 
5.6%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
2
 
1.9%
Other values (19) 38
35.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 108
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
20
18.5%
16
14.8%
6
 
5.6%
6
 
5.6%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
2
 
1.9%
Other values (19) 38
35.2%

성별
Categorical

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
18 
18 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
18
50.0%
18
50.0%

Length

2023-12-11T09:58:07.302111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:58:07.395685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
18
50.0%
18
50.0%

지체
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2617.9167
Minimum699
Maximum15523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T09:58:07.507431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum699
5-th percentile862
Q11155.5
median1377
Q32385.75
95-th percentile8384.25
Maximum15523
Range14824
Interquartile range (IQR)1230.25

Descriptive statistics

Standard deviation2983.8199
Coefficient of variation (CV)1.1397689
Kurtosis10.215058
Mean2617.9167
Median Absolute Deviation (MAD)424
Skewness3.0101576
Sum94245
Variance8903181.2
MonotonicityNot monotonic
2023-12-11T09:58:07.642602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
15523 1
 
2.8%
1239 1
 
2.8%
1212 1
 
2.8%
1320 1
 
2.8%
1123 1
 
2.8%
1114 1
 
2.8%
1154 1
 
2.8%
1267 1
 
2.8%
1217 1
 
2.8%
902 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
699 1
2.8%
742 1
2.8%
902 1
2.8%
905 1
2.8%
928 1
2.8%
981 1
2.8%
1114 1
2.8%
1123 1
2.8%
1154 1
2.8%
1156 1
2.8%
ValueCountFrequency (%)
15523 1
2.8%
9927 1
2.8%
7870 1
2.8%
5095 1
2.8%
4423 1
2.8%
4397 1
2.8%
4035 1
2.8%
3726 1
2.8%
2694 1
2.8%
2283 1
2.8%

시각
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean474.08333
Minimum115
Maximum2796
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T09:58:07.836696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum115
5-th percentile139
Q1184.5
median270
Q3487.25
95-th percentile1423.5
Maximum2796
Range2681
Interquartile range (IQR)302.75

Descriptive statistics

Standard deviation540.05393
Coefficient of variation (CV)1.139154
Kurtosis9.9278795
Mean474.08333
Median Absolute Deviation (MAD)105.5
Skewness2.9493838
Sum17067
Variance291658.25
MonotonicityNot monotonic
2023-12-11T09:58:07.994618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
172 2
 
5.6%
1824 1
 
2.8%
283 1
 
2.8%
206 1
 
2.8%
245 1
 
2.8%
151 1
 
2.8%
221 1
 
2.8%
177 1
 
2.8%
242 1
 
2.8%
2796 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
115 1
2.8%
136 1
2.8%
140 1
2.8%
148 1
2.8%
151 1
2.8%
170 1
2.8%
172 2
5.6%
177 1
2.8%
187 1
2.8%
199 1
2.8%
ValueCountFrequency (%)
2796 1
2.8%
1824 1
2.8%
1290 1
2.8%
989 1
2.8%
930 1
2.8%
887 1
2.8%
606 1
2.8%
593 1
2.8%
509 1
2.8%
480 1
2.8%

청각
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean568.91667
Minimum151
Maximum2832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T09:58:08.419512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum151
5-th percentile194.75
Q1264
median342
Q3723.25
95-th percentile1580.75
Maximum2832
Range2681
Interquartile range (IQR)459.25

Descriptive statistics

Standard deviation578.07184
Coefficient of variation (CV)1.0160923
Kurtosis8.0455564
Mean568.91667
Median Absolute Deviation (MAD)99
Skewness2.7417371
Sum20481
Variance334167.05
MonotonicityNot monotonic
2023-12-11T09:58:08.561833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
243 2
 
5.6%
319 2
 
5.6%
2832 1
 
2.8%
255 1
 
2.8%
322 1
 
2.8%
365 1
 
2.8%
224 1
 
2.8%
222 1
 
2.8%
347 1
 
2.8%
204 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
151 1
2.8%
167 1
2.8%
204 1
2.8%
222 1
2.8%
224 1
2.8%
237 1
2.8%
243 2
5.6%
255 1
2.8%
267 1
2.8%
273 1
2.8%
ValueCountFrequency (%)
2832 1
2.8%
2363 1
2.8%
1320 1
2.8%
1159 1
2.8%
934 1
2.8%
881 1
2.8%
879 1
2.8%
798 1
2.8%
757 1
2.8%
712 1
2.8%

언어
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.805556
Minimum5
Maximum236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T09:58:08.711822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q113.75
median23
Q338
95-th percentile111.75
Maximum236
Range231
Interquartile range (IQR)24.25

Descriptive statistics

Standard deviation44.87495
Coefficient of variation (CV)1.1869935
Kurtosis10.554431
Mean37.805556
Median Absolute Deviation (MAD)10.5
Skewness2.9761749
Sum1361
Variance2013.7611
MonotonicityNot monotonic
2023-12-11T09:58:08.826872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
23 4
 
11.1%
12 2
 
5.6%
6 2
 
5.6%
19 2
 
5.6%
27 2
 
5.6%
11 2
 
5.6%
236 1
 
2.8%
13 1
 
2.8%
33 1
 
2.8%
18 1
 
2.8%
Other values (18) 18
50.0%
ValueCountFrequency (%)
5 1
2.8%
6 2
5.6%
7 1
2.8%
11 2
5.6%
12 2
5.6%
13 1
2.8%
14 1
2.8%
17 1
2.8%
18 1
2.8%
19 2
5.6%
ValueCountFrequency (%)
236 1
2.8%
123 1
2.8%
108 1
2.8%
104 1
2.8%
88 1
2.8%
60 1
2.8%
46 1
2.8%
44 1
2.8%
41 1
2.8%
37 1
2.8%

지적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean406.33333
Minimum116
Maximum1964
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T09:58:08.967471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum116
5-th percentile147.25
Q1170.5
median241
Q3427.75
95-th percentile1189.75
Maximum1964
Range1848
Interquartile range (IQR)257.25

Descriptive statistics

Standard deviation394.31988
Coefficient of variation (CV)0.9704345
Kurtosis6.6846613
Mean406.33333
Median Absolute Deviation (MAD)85.5
Skewness2.4591736
Sum14628
Variance155488.17
MonotonicityNot monotonic
2023-12-11T09:58:09.098024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1964 1
 
2.8%
150 1
 
2.8%
171 1
 
2.8%
234 1
 
2.8%
179 1
 
2.8%
208 1
 
2.8%
163 1
 
2.8%
224 1
 
2.8%
173 1
 
2.8%
155 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
116 1
2.8%
139 1
2.8%
150 1
2.8%
154 1
2.8%
155 1
2.8%
156 1
2.8%
163 1
2.8%
166 1
2.8%
169 1
2.8%
171 1
2.8%
ValueCountFrequency (%)
1964 1
2.8%
1342 1
2.8%
1139 1
2.8%
933 1
2.8%
757 1
2.8%
696 1
2.8%
607 1
2.8%
596 1
2.8%
448 1
2.8%
421 1
2.8%

뇌병변
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean492.05556
Minimum128
Maximum2859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T09:58:09.259839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128
5-th percentile143.5
Q1186.5
median250
Q3477.75
95-th percentile1539
Maximum2859
Range2731
Interquartile range (IQR)291.25

Descriptive statistics

Standard deviation588.65323
Coefficient of variation (CV)1.1963146
Kurtosis8.5089608
Mean492.05556
Median Absolute Deviation (MAD)95.5
Skewness2.8171778
Sum17714
Variance346512.63
MonotonicityNot monotonic
2023-12-11T09:58:09.418976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
153 2
 
5.6%
206 2
 
5.6%
2859 1
 
2.8%
187 1
 
2.8%
288 1
 
2.8%
233 1
 
2.8%
190 1
 
2.8%
185 1
 
2.8%
204 1
 
2.8%
160 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
128 1
2.8%
133 1
2.8%
147 1
2.8%
153 2
5.6%
160 1
2.8%
164 1
2.8%
182 1
2.8%
185 1
2.8%
187 1
2.8%
190 1
2.8%
ValueCountFrequency (%)
2859 1
2.8%
2250 1
2.8%
1302 1
2.8%
992 1
2.8%
989 1
2.8%
914 1
2.8%
797 1
2.8%
700 1
2.8%
519 1
2.8%
464 1
2.8%
Distinct27
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-11T09:58:09.621998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.75
Min length1

Characters and Unicode

Total characters63
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)55.6%

Sample

1st row443
2nd row102
3rd row130
4th row22
5th row31
ValueCountFrequency (%)
5 3
 
8.3%
2 3
 
8.3%
19 2
 
5.6%
12 2
 
5.6%
33 2
 
5.6%
1 2
 
5.6%
4 2
 
5.6%
118 1
 
2.8%
32 1
 
2.8%
18 1
 
2.8%
Other values (17) 17
47.2%
2023-12-11T09:58:10.015069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 17
27.0%
2 11
17.5%
3 10
15.9%
5 7
11.1%
4 5
 
7.9%
9 3
 
4.8%
6 3
 
4.8%
0 2
 
3.2%
7 2
 
3.2%
8 2
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62
98.4%
Dash Punctuation 1
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17
27.4%
2 11
17.7%
3 10
16.1%
5 7
11.3%
4 5
 
8.1%
9 3
 
4.8%
6 3
 
4.8%
0 2
 
3.2%
7 2
 
3.2%
8 2
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 17
27.0%
2 11
17.5%
3 10
15.9%
5 7
11.1%
4 5
 
7.9%
9 3
 
4.8%
6 3
 
4.8%
0 2
 
3.2%
7 2
 
3.2%
8 2
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 17
27.0%
2 11
17.5%
3 10
15.9%
5 7
11.1%
4 5
 
7.9%
9 3
 
4.8%
6 3
 
4.8%
0 2
 
3.2%
7 2
 
3.2%
8 2
 
3.2%

정신
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.97222
Minimum57
Maximum1007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T09:58:10.162297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile65.5
Q1100.5
median129
Q3198.75
95-th percentile585
Maximum1007
Range950
Interquartile range (IQR)98.25

Descriptive statistics

Standard deviation217.37847
Coefficient of variation (CV)1.0065112
Kurtosis6.4912472
Mean215.97222
Median Absolute Deviation (MAD)42.5
Skewness2.5071819
Sum7775
Variance47253.399
MonotonicityNot monotonic
2023-12-11T09:58:10.322679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
83 2
 
5.6%
136 2
 
5.6%
1007 1
 
2.8%
111 1
 
2.8%
102 1
 
2.8%
177 1
 
2.8%
91 1
 
2.8%
57 1
 
2.8%
122 1
 
2.8%
93 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
57 1
2.8%
64 1
2.8%
66 1
2.8%
83 2
5.6%
84 1
2.8%
91 1
2.8%
93 1
2.8%
99 1
2.8%
101 1
2.8%
102 1
2.8%
ValueCountFrequency (%)
1007 1
2.8%
912 1
2.8%
476 1
2.8%
447 1
2.8%
426 1
2.8%
425 1
2.8%
390 1
2.8%
311 1
2.8%
210 1
2.8%
195 1
2.8%

신장
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.5
Minimum15
Maximum954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T09:58:10.474372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile29.5
Q141.25
median77.5
Q3145.25
95-th percentile489.75
Maximum954
Range939
Interquartile range (IQR)104

Descriptive statistics

Standard deviation197.2997
Coefficient of variation (CV)1.3109615
Kurtosis8.5104849
Mean150.5
Median Absolute Deviation (MAD)45
Skewness2.7827013
Sum5418
Variance38927.171
MonotonicityNot monotonic
2023-12-11T09:58:10.635895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
32 2
 
5.6%
47 2
 
5.6%
39 2
 
5.6%
28 1
 
2.8%
100 1
 
2.8%
42 1
 
2.8%
79 1
 
2.8%
68 1
 
2.8%
954 1
 
2.8%
74 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
15 1
2.8%
28 1
2.8%
30 1
2.8%
32 2
5.6%
33 1
2.8%
35 1
2.8%
39 2
5.6%
42 1
2.8%
43 1
2.8%
47 2
5.6%
ValueCountFrequency (%)
954 1
2.8%
711 1
2.8%
416 1
2.8%
372 1
2.8%
302 1
2.8%
284 1
2.8%
236 1
2.8%
213 1
2.8%
167 1
2.8%
138 1
2.8%

심장
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Memory size420.0 B
4
1
2
3
6
Other values (10)
12 

Length

Max length2
Median length1
Mean length1.2222222
Min length1

Unique

Unique8 ?
Unique (%)22.2%

Sample

1st row45
2nd row29
3rd row18
4th row6
5th row7

Common Values

ValueCountFrequency (%)
4 7
19.4%
1 6
16.7%
2 4
11.1%
3 4
11.1%
6 3
8.3%
18 2
 
5.6%
7 2
 
5.6%
45 1
 
2.8%
29 1
 
2.8%
13 1
 
2.8%
Other values (5) 5
13.9%

Length

2023-12-11T09:58:10.771007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4 7
19.4%
1 6
16.7%
2 4
11.1%
3 4
11.1%
6 3
8.3%
18 2
 
5.6%
7 2
 
5.6%
45 1
 
2.8%
29 1
 
2.8%
13 1
 
2.8%
Other values (5) 5
13.9%

호흡기
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.888889
Minimum2
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T09:58:10.889073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.75
Q16
median13
Q324.5
95-th percentile66.75
Maximum110
Range108
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation24.501636
Coefficient of variation (CV)1.1193641
Kurtosis4.0219921
Mean21.888889
Median Absolute Deviation (MAD)8
Skewness1.9737954
Sum788
Variance600.33016
MonotonicityNot monotonic
2023-12-11T09:58:11.045096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
7 3
 
8.3%
6 3
 
8.3%
11 2
 
5.6%
17 2
 
5.6%
5 2
 
5.6%
3 2
 
5.6%
2 2
 
5.6%
4 2
 
5.6%
110 1
 
2.8%
10 1
 
2.8%
Other values (16) 16
44.4%
ValueCountFrequency (%)
2 2
5.6%
3 2
5.6%
4 2
5.6%
5 2
5.6%
6 3
8.3%
7 3
8.3%
9 1
 
2.8%
10 1
 
2.8%
11 2
5.6%
15 1
 
2.8%
ValueCountFrequency (%)
110 1
2.8%
75 1
2.8%
64 1
2.8%
63 1
2.8%
58 1
2.8%
41 1
2.8%
40 1
2.8%
31 1
2.8%
29 1
2.8%
23 1
2.8%


Text

Distinct23
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-11T09:58:11.224575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2.5
Mean length1.5277778
Min length1

Characters and Unicode

Total characters55
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)36.1%

Sample

1st row130
2nd row49
3rd row44
4th row17
5th row22
ValueCountFrequency (%)
4 3
 
8.3%
8 3
 
8.3%
2 3
 
8.3%
16 2
 
5.6%
6 2
 
5.6%
22 2
 
5.6%
7 2
 
5.6%
3 2
 
5.6%
14 2
 
5.6%
1 2
 
5.6%
Other values (13) 13
36.1%
2023-12-11T09:58:11.627049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 11
20.0%
1 11
20.0%
4 10
18.2%
6 5
9.1%
8 4
 
7.3%
3 4
 
7.3%
7 3
 
5.5%
5 2
 
3.6%
9 2
 
3.6%
0 2
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54
98.2%
Dash Punctuation 1
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 11
20.4%
1 11
20.4%
4 10
18.5%
6 5
9.3%
8 4
 
7.4%
3 4
 
7.4%
7 3
 
5.6%
5 2
 
3.7%
9 2
 
3.7%
0 2
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 55
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 11
20.0%
1 11
20.0%
4 10
18.2%
6 5
9.1%
8 4
 
7.3%
3 4
 
7.3%
7 3
 
5.5%
5 2
 
3.6%
9 2
 
3.6%
0 2
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 11
20.0%
1 11
20.0%
4 10
18.2%
6 5
9.1%
8 4
 
7.3%
3 4
 
7.3%
7 3
 
5.5%
5 2
 
3.6%
9 2
 
3.6%
0 2
 
3.6%

안면
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Memory size420.0 B
3
-
2
7
6
Other values (10)
14 

Length

Max length2
Median length1
Mean length1.1388889
Min length1

Unique

Unique6 ?
Unique (%)16.7%

Sample

1st row39
2nd row21
3rd row7
4th row10
5th row6

Common Values

ValueCountFrequency (%)
3 6
16.7%
- 6
16.7%
2 4
11.1%
7 3
8.3%
6 3
8.3%
8 2
 
5.6%
9 2
 
5.6%
5 2
 
5.6%
1 2
 
5.6%
39 1
 
2.8%
Other values (5) 5
13.9%

Length

2023-12-11T09:58:11.803542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3 6
16.7%
6
16.7%
2 4
11.1%
7 3
8.3%
6 3
8.3%
8 2
 
5.6%
9 2
 
5.6%
5 2
 
5.6%
1 2
 
5.6%
39 1
 
2.8%
Other values (5) 5
13.9%

장루.요루
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.194444
Minimum1
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T09:58:11.950056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111.75
median16.5
Q335.25
95-th percentile64
Maximum144
Range143
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation26.988695
Coefficient of variation (CV)1.0303214
Kurtosis9.7256822
Mean26.194444
Median Absolute Deviation (MAD)6.5
Skewness2.7427464
Sum943
Variance728.38968
MonotonicityNot monotonic
2023-12-11T09:58:12.074625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
16 4
 
11.1%
18 3
 
8.3%
60 2
 
5.6%
12 2
 
5.6%
9 2
 
5.6%
20 2
 
5.6%
10 2
 
5.6%
3 2
 
5.6%
144 1
 
2.8%
1 1
 
2.8%
Other values (15) 15
41.7%
ValueCountFrequency (%)
1 1
2.8%
3 2
5.6%
8 1
2.8%
9 2
5.6%
10 2
5.6%
11 1
2.8%
12 2
5.6%
13 1
2.8%
14 1
2.8%
15 1
2.8%
ValueCountFrequency (%)
144 1
2.8%
73 1
2.8%
61 1
2.8%
60 2
5.6%
45 1
2.8%
40 1
2.8%
39 1
2.8%
36 1
2.8%
35 1
2.8%
23 1
2.8%

뇌전증
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.694444
Minimum1
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T09:58:12.226974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5.5
Q312.25
95-th percentile33.25
Maximum63
Range62
Interquartile range (IQR)9.25

Descriptive statistics

Standard deviation13.67964
Coefficient of variation (CV)1.2791351
Kurtosis7.8958254
Mean10.694444
Median Absolute Deviation (MAD)3.5
Skewness2.705274
Sum385
Variance187.13254
MonotonicityNot monotonic
2023-12-11T09:58:12.353740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 5
13.9%
3 4
11.1%
4 3
 
8.3%
5 3
 
8.3%
12 3
 
8.3%
9 3
 
8.3%
1 3
 
8.3%
6 2
 
5.6%
55 1
 
2.8%
16 1
 
2.8%
Other values (8) 8
22.2%
ValueCountFrequency (%)
1 3
8.3%
2 5
13.9%
3 4
11.1%
4 3
8.3%
5 3
8.3%
6 2
 
5.6%
7 1
 
2.8%
9 3
8.3%
12 3
8.3%
13 1
 
2.8%
ValueCountFrequency (%)
63 1
 
2.8%
55 1
 
2.8%
26 1
 
2.8%
25 1
 
2.8%
22 1
 
2.8%
17 1
 
2.8%
16 1
 
2.8%
14 1
 
2.8%
13 1
 
2.8%
12 3
8.3%

Interactions

2023-12-11T09:58:05.802068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:55.499032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:56.878547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:58.036514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:58.927504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:59.851048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:00.822704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:01.810556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:03.124500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:04.082704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:05.000435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:05.877974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:55.593533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:56.978411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:58.120166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:59.029634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:59.952911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:00.915387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:01.947420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:03.207995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:04.188456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:05.087310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:05.940823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:55.688367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:57.071184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:58.196356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:59.113221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:00.047693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:00.989950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:02.035456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:03.289187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:04.267340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:05.161680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:06.003944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:55.774390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:57.160141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:58.263405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:59.185353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:00.133399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:01.065919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:02.109822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:03.366091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:04.334850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:05.247320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:06.066361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:55.876930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:57.243761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:58.347101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:59.254958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:00.216896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:01.156296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:02.199862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:03.453392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:04.419668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:05.313079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:06.134152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:55.988652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:57.451468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:58.439139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:59.348592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:00.302949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:01.277389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:02.300429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:03.544772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:04.515604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:05.396168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:06.196213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:56.077171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:57.570676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:58.521895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:59.424234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:00.379972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:01.355835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:02.397494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:03.613559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:04.589774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:05.470135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:06.256455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:56.160534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:57.665864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:58.590496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:59.494826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:00.448008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:01.434653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:02.478338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:03.719476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:04.660033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:05.535268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:06.318864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:56.254536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:57.761809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:58.668365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:59.593690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:00.554720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:01.512442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:02.570089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:03.800451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:04.732661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:05.599589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:06.384121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:56.347806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:57.834840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:58.759326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:59.669083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:00.627261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:01.604399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:02.647038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:03.891598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:04.815048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:05.664144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:06.451677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:56.501410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:57.947717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:58.838686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:57:59.760501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:00.733158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:01.692376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:02.759140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:03.988701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:04.905421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:58:05.733660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:58:12.484174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분성별지체시각청각언어지적뇌병변자폐성정신신장심장호흡기안면장루.요루뇌전증
구 분1.0000.0000.6020.5790.7220.3130.5860.7900.6340.7100.7410.2590.0000.4090.0000.5660.764
성별0.0001.0000.0000.0700.0000.0000.0000.0000.6610.0000.0000.0000.2790.8000.5030.2070.000
지체0.6020.0001.0000.9480.9270.8290.9740.9301.0000.8860.9390.9200.8690.9890.9810.8980.932
시각0.5790.0700.9481.0000.9920.9600.9550.9910.9950.9610.9910.9420.9790.9890.9440.9730.985
청각0.7220.0000.9270.9921.0000.9620.9330.9880.9930.9590.9840.9590.9700.9940.9860.9580.985
언어0.3130.0000.8290.9600.9621.0000.9190.9540.9940.9170.9540.9480.9400.9920.8930.9290.948
지적0.5860.0000.9740.9550.9330.9191.0000.9520.9820.8860.9450.9330.8730.9930.9320.8870.932
뇌병변0.7900.0000.9300.9910.9880.9540.9521.0000.9900.9590.9910.9300.9590.9900.9240.9630.983
자폐성0.6340.6611.0000.9950.9930.9940.9820.9901.0000.9840.9870.8820.9960.9450.9000.9790.988
정신0.7100.0000.8860.9610.9590.9170.8860.9590.9841.0000.9840.9300.9270.9860.8680.9760.993
신장0.7410.0000.9390.9910.9840.9540.9450.9910.9870.9841.0000.9200.9590.9930.9170.9600.998
심장0.2590.0000.9200.9420.9590.9480.9330.9300.8820.9300.9201.0000.9130.9200.9260.9510.926
호흡기0.0000.2790.8690.9790.9700.9400.8730.9590.9960.9270.9590.9131.0000.9860.9100.9590.951
0.4090.8000.9890.9890.9940.9920.9930.9900.9450.9860.9930.9200.9861.0000.9000.9620.993
안면0.0000.5030.9810.9440.9860.8930.9320.9240.9000.8680.9170.9260.9100.9001.0000.9000.915
장루.요루0.5660.2070.8980.9730.9580.9290.8870.9630.9790.9760.9600.9510.9590.9620.9001.0000.957
뇌전증0.7640.0000.9320.9850.9850.9480.9320.9830.9880.9930.9980.9260.9510.9930.9150.9571.000
2023-12-11T09:58:12.651581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
심장안면성별
심장1.0000.4850.000
안면0.4851.0000.349
성별0.0000.3491.000
2023-12-11T09:58:12.768697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지체시각청각언어지적뇌병변정신신장호흡기장루.요루뇌전증성별심장안면
지체1.0000.9610.8960.7640.9100.9510.8080.9530.7580.8530.8500.0000.6360.730
시각0.9611.0000.8850.8570.9550.9550.8410.9490.8340.9100.8970.0000.6760.680
청각0.8960.8851.0000.6800.8190.9180.7190.8360.6480.7970.8420.0000.7230.737
언어0.7640.8570.6801.0000.8200.7890.7370.8400.9050.8320.7610.0000.6900.574
지적0.9100.9550.8190.8201.0000.9010.8430.9180.8210.8570.8880.0000.6530.650
뇌병변0.9510.9550.9180.7890.9011.0000.7720.9210.7540.8500.8520.0000.6480.634
정신0.8080.8410.7190.7370.8430.7721.0000.8290.6980.7560.8140.0000.6470.530
신장0.9530.9490.8360.8400.9180.9210.8291.0000.8280.8430.8700.0000.6270.620
호흡기0.7580.8340.6480.9050.8210.7540.6980.8281.0000.8550.7780.2670.6110.605
장루.요루0.8530.9100.7970.8320.8570.8500.7560.8430.8551.0000.8660.1930.6990.587
뇌전증0.8500.8970.8420.7610.8880.8520.8140.8700.7780.8661.0000.0000.6400.615
성별0.0000.0000.0000.0000.0000.0000.0000.0000.2670.1930.0001.0000.0000.349
심장0.6360.6760.7230.6900.6530.6480.6470.6270.6110.6990.6400.0001.0000.485
안면0.7300.6800.7370.5740.6500.6340.5300.6200.6050.5870.6150.3490.4851.000

Missing values

2023-12-11T09:58:06.548283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:58:06.716740image/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

구 분성별지체시각청각언어지적뇌병변자폐성정신신장심장호흡기안면장루.요루뇌전증
0창원시1552327962832236196428594431007954451101303914455
1창원시99271824236310413422250102912711296449217363
2진주시509598993412393391413047637218634476025
3진주시3726606879416077972242523662317103914
4통영시228343840037344396311161677172262212
5통영시171933232523245292121341031748186
6사천시215138138846421348331951336202232312
7사천시1579300393172752626172802378119
8김해시78701290132010811391302275426416187545236126
9김해시44238871159446969925544730213312894022
구 분성별지체시각청각언어지적뇌병변자폐성정신신장심장호흡기안면장루.요루뇌전증
26하동군1267242243232241871912250-22146205
27하동군121720025511173160293354533161
28산청군9021702042315515315844711116-123
29산청군90514023714154164-663012-2123
30함양군9281992432416615313136323781134
31함양군98113626712116133212039166-102
32거창군140426432418246227181137649101164
33거창군12641723371215618249953154-82
34합천군11562593143323322971484341185183
35합천군12011873696169206510133332396