Overview

Dataset statistics

Number of variables13
Number of observations673
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory72.4 KiB
Average record size in memory110.2 B

Variable types

Categorical4
Text3
Numeric6

Dataset

Description경기도 고양시_등록장애인현황 데이터는 고양시 15개 장애유형별 등록장애인 현황데이터로 등록장애인수(합계,남성,여성), 심한장애인수(합계,남성,여성), 심하지않은장애인수(합계,남성,여성) 등의 항목을 제공합니다.
Author경기도 고양시
URLhttps://www.data.go.kr/data/15078669/fileData.do

Alerts

시군명 has constant value ""Constant
읍면동명 is highly overall correlated with 구명High correlation
구명 is highly overall correlated with 읍면동명High correlation
등록장애인수(여성) is highly overall correlated with 심한장애인수(합계) and 4 other fieldsHigh correlation
심한장애인수(합계) is highly overall correlated with 등록장애인수(여성) and 2 other fieldsHigh correlation
심한장애인수(남성) is highly overall correlated with 등록장애인수(여성) and 2 other fieldsHigh correlation
심한장애인수(여성) is highly overall correlated with 등록장애인수(여성) and 2 other fieldsHigh correlation
심하지않은장애인수(남성) is highly overall correlated with 등록장애인수(여성) and 1 other fieldsHigh correlation
심하지않은장애인수(여성) is highly overall correlated with 등록장애인수(여성) and 1 other fieldsHigh correlation
등록장애인수(여성) has 63 (9.4%) zerosZeros
심한장애인수(합계) has 86 (12.8%) zerosZeros
심한장애인수(남성) has 122 (18.1%) zerosZeros
심한장애인수(여성) has 160 (23.8%) zerosZeros
심하지않은장애인수(남성) has 217 (32.2%) zerosZeros
심하지않은장애인수(여성) has 254 (37.7%) zerosZeros

Reproduction

Analysis started2023-12-12 00:19:24.442412
Analysis finished2023-12-12 00:19:28.988738
Duration4.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
고양시
673 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고양시 673
100.0%

Length

2023-12-12T09:19:29.091112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:19:29.196931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고양시 673
100.0%

구명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
덕양구
323 
일산동구
180 
일산서구
170 

Length

Max length4
Median length4
Mean length3.5200594
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
덕양구 323
48.0%
일산동구 180
26.7%
일산서구 170
25.3%

Length

2023-12-12T09:19:29.323811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:19:29.439506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
덕양구 323
48.0%
일산동구 180
26.7%
일산서구 170
25.3%

읍면동명
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
주교동
 
16
화정2동
 
16
탄현1동
 
16
풍산동
 
16
흥도동
 
16
Other values (39)
593 

Length

Max length4
Median length4
Mean length3.5854383
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
주교동 16
 
2.4%
화정2동 16
 
2.4%
탄현1동 16
 
2.4%
풍산동 16
 
2.4%
흥도동 16
 
2.4%
덕이동 16
 
2.4%
대화동 16
 
2.4%
주엽2동 16
 
2.4%
고양동 16
 
2.4%
관산동 16
 
2.4%
Other values (34) 513
76.2%

Length

2023-12-12T09:19:29.588035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주교동 16
 
2.4%
백석1동 16
 
2.4%
화정2동 16
 
2.4%
정발산동 16
 
2.4%
일산3동 16
 
2.4%
중산2동 16
 
2.4%
식사동 16
 
2.4%
고봉동 16
 
2.4%
행신4동 16
 
2.4%
행신1동 16
 
2.4%
Other values (34) 513
76.2%

장애유형
Categorical

Distinct16
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
소계
 
44
지체
 
44
시각
 
44
청각
 
44
언어
 
44
Other values (11)
453 

Length

Max length5
Median length2
Mean length2.3774146
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소계
2nd row지체
3rd row시각
4th row청각
5th row언어

Common Values

ValueCountFrequency (%)
소계 44
 
6.5%
지체 44
 
6.5%
시각 44
 
6.5%
청각 44
 
6.5%
언어 44
 
6.5%
지적 44
 
6.5%
뇌병변 44
 
6.5%
자폐성 44
 
6.5%
정신 44
 
6.5%
신장 44
 
6.5%
Other values (6) 233
34.6%

Length

2023-12-12T09:19:29.762372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
소계 44
 
6.5%
지체 44
 
6.5%
시각 44
 
6.5%
청각 44
 
6.5%
언어 44
 
6.5%
지적 44
 
6.5%
뇌병변 44
 
6.5%
자폐성 44
 
6.5%
정신 44
 
6.5%
신장 44
 
6.5%
Other values (6) 233
34.6%

등록장애인수(합계)
Text

Distinct230
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
2023-12-12T09:19:30.231345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length1.9524517
Min length1

Characters and Unicode

Total characters1314
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

Unique137 ?
Unique (%)20.4%

Sample

1st row672
2nd row301
3rd row75
4th row93
5th row7
ValueCountFrequency (%)
1 46
 
6.8%
4 32
 
4.8%
3 29
 
4.3%
2 26
 
3.9%
7 23
 
3.4%
5 23
 
3.4%
6 21
 
3.1%
8 20
 
3.0%
9 15
 
2.2%
11 11
 
1.6%
Other values (220) 427
63.4%
2023-12-12T09:19:30.813540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 297
22.6%
3 147
11.2%
4 146
11.1%
2 146
11.1%
5 120
9.1%
9 95
 
7.2%
6 93
 
7.1%
8 93
 
7.1%
7 89
 
6.8%
0 84
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1310
99.7%
Other Punctuation 4
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 297
22.7%
3 147
11.2%
4 146
11.1%
2 146
11.1%
5 120
9.2%
9 95
 
7.3%
6 93
 
7.1%
8 93
 
7.1%
7 89
 
6.8%
0 84
 
6.4%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1314
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 297
22.6%
3 147
11.2%
4 146
11.1%
2 146
11.1%
5 120
9.1%
9 95
 
7.2%
6 93
 
7.1%
8 93
 
7.1%
7 89
 
6.8%
0 84
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 297
22.6%
3 147
11.2%
4 146
11.1%
2 146
11.1%
5 120
9.1%
9 95
 
7.2%
6 93
 
7.1%
8 93
 
7.1%
7 89
 
6.8%
0 84
 
6.4%

등록장애인수(남성)
Text

Distinct180
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
2023-12-12T09:19:31.156326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length1.7355126
Min length1

Characters and Unicode

Total characters1168
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

Unique94 ?
Unique (%)14.0%

Sample

1st row398
2nd row166
3rd row50
4th row52
5th row5
ValueCountFrequency (%)
3 42
 
6.2%
1 38
 
5.6%
2 36
 
5.3%
5 35
 
5.2%
0 33
 
4.9%
4 31
 
4.6%
6 24
 
3.6%
7 16
 
2.4%
9 16
 
2.4%
11 11
 
1.6%
Other values (170) 391
58.1%
2023-12-12T09:19:31.639980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 174
14.9%
2 160
13.7%
3 145
12.4%
5 138
11.8%
4 129
11.0%
0 92
7.9%
7 91
7.8%
6 90
7.7%
9 77
6.6%
8 71
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1167
99.9%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 174
14.9%
2 160
13.7%
3 145
12.4%
5 138
11.8%
4 129
11.1%
0 92
7.9%
7 91
7.8%
6 90
7.7%
9 77
6.6%
8 71
6.1%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 174
14.9%
2 160
13.7%
3 145
12.4%
5 138
11.8%
4 129
11.0%
0 92
7.9%
7 91
7.8%
6 90
7.7%
9 77
6.6%
8 71
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 174
14.9%
2 160
13.7%
3 145
12.4%
5 138
11.8%
4 129
11.0%
0 92
7.9%
7 91
7.8%
6 90
7.7%
9 77
6.6%
8 71
6.1%

등록장애인수(여성)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct161
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.077266
Minimum0
Maximum980
Zeros63
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-12-12T09:19:31.788538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median10
Q344
95-th percentile307
Maximum980
Range980
Interquartile range (IQR)42

Descriptive statistics

Standard deviation113.82022
Coefficient of variation (CV)2.1047702
Kurtosis14.933644
Mean54.077266
Median Absolute Deviation (MAD)9
Skewness3.5331026
Sum36394
Variance12955.042
MonotonicityNot monotonic
2023-12-12T09:19:31.939490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 92
 
13.7%
0 63
 
9.4%
2 60
 
8.9%
3 44
 
6.5%
4 26
 
3.9%
5 19
 
2.8%
6 11
 
1.6%
15 11
 
1.6%
17 10
 
1.5%
8 8
 
1.2%
Other values (151) 329
48.9%
ValueCountFrequency (%)
0 63
9.4%
1 92
13.7%
2 60
8.9%
3 44
6.5%
4 26
 
3.9%
5 19
 
2.8%
6 11
 
1.6%
7 6
 
0.9%
8 8
 
1.2%
9 4
 
0.6%
ValueCountFrequency (%)
980 1
0.1%
726 1
0.1%
672 1
0.1%
629 1
0.1%
621 1
0.1%
604 1
0.1%
541 1
0.1%
531 1
0.1%
487 1
0.1%
480 1
0.1%

심한장애인수(합계)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct147
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.934621
Minimum0
Maximum802
Zeros86
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-12-12T09:19:32.093106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median18
Q348
95-th percentile265.6
Maximum802
Range802
Interquartile range (IQR)46

Descriptive statistics

Standard deviation97.123319
Coefficient of variation (CV)2.0261622
Kurtosis17.791224
Mean47.934621
Median Absolute Deviation (MAD)17
Skewness3.9173803
Sum32260
Variance9432.9392
MonotonicityNot monotonic
2023-12-12T09:19:32.500216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 86
 
12.8%
1 62
 
9.2%
2 43
 
6.4%
4 26
 
3.9%
3 22
 
3.3%
7 13
 
1.9%
34 11
 
1.6%
5 11
 
1.6%
30 11
 
1.6%
6 10
 
1.5%
Other values (137) 378
56.2%
ValueCountFrequency (%)
0 86
12.8%
1 62
9.2%
2 43
6.4%
3 22
 
3.3%
4 26
 
3.9%
5 11
 
1.6%
6 10
 
1.5%
7 13
 
1.9%
8 8
 
1.2%
9 6
 
0.9%
ValueCountFrequency (%)
802 1
0.1%
681 1
0.1%
654 1
0.1%
617 1
0.1%
565 1
0.1%
546 1
0.1%
526 1
0.1%
469 1
0.1%
457 1
0.1%
446 1
0.1%

심한장애인수(남성)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct113
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.573551
Minimum0
Maximum474
Zeros122
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-12-12T09:19:32.636577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10
Q328
95-th percentile167.2
Maximum474
Range474
Interquartile range (IQR)27

Descriptive statistics

Standard deviation57.697443
Coefficient of variation (CV)2.0192605
Kurtosis17.116606
Mean28.573551
Median Absolute Deviation (MAD)10
Skewness3.8541358
Sum19230
Variance3328.995
MonotonicityNot monotonic
2023-12-12T09:19:32.774056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 122
 
18.1%
1 62
 
9.2%
2 34
 
5.1%
3 26
 
3.9%
4 24
 
3.6%
11 18
 
2.7%
5 17
 
2.5%
6 16
 
2.4%
15 15
 
2.2%
16 13
 
1.9%
Other values (103) 326
48.4%
ValueCountFrequency (%)
0 122
18.1%
1 62
9.2%
2 34
 
5.1%
3 26
 
3.9%
4 24
 
3.6%
5 17
 
2.5%
6 16
 
2.4%
7 13
 
1.9%
8 8
 
1.2%
9 10
 
1.5%
ValueCountFrequency (%)
474 1
0.1%
406 1
0.1%
360 1
0.1%
353 1
0.1%
348 1
0.1%
331 1
0.1%
289 1
0.1%
278 1
0.1%
272 2
0.3%
260 1
0.1%

심한장애인수(여성)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.36107
Minimum0
Maximum328
Zeros160
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-12-12T09:19:32.927878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q319
95-th percentile111.2
Maximum328
Range328
Interquartile range (IQR)18

Descriptive statistics

Standard deviation39.987447
Coefficient of variation (CV)2.0653532
Kurtosis18.949948
Mean19.36107
Median Absolute Deviation (MAD)6
Skewness3.9966674
Sum13030
Variance1598.9959
MonotonicityNot monotonic
2023-12-12T09:19:33.128890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 160
23.8%
1 84
 
12.5%
2 34
 
5.1%
3 24
 
3.6%
10 18
 
2.7%
11 16
 
2.4%
6 15
 
2.2%
14 15
 
2.2%
7 15
 
2.2%
13 15
 
2.2%
Other values (83) 277
41.2%
ValueCountFrequency (%)
0 160
23.8%
1 84
12.5%
2 34
 
5.1%
3 24
 
3.6%
4 15
 
2.2%
5 11
 
1.6%
6 15
 
2.2%
7 15
 
2.2%
8 12
 
1.8%
9 14
 
2.1%
ValueCountFrequency (%)
328 1
0.1%
301 1
0.1%
275 1
0.1%
269 1
0.1%
248 1
0.1%
215 1
0.1%
205 1
0.1%
188 1
0.1%
186 1
0.1%
185 1
0.1%

심하지않은장애인수(합계)
Text

Distinct182
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
2023-12-12T09:19:33.452819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.5928678
Min length1

Characters and Unicode

Total characters1072
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

Unique124 ?
Unique (%)18.4%

Sample

1st row435
2nd row258
3rd row62
4th row71
5th row3
ValueCountFrequency (%)
0 180
26.7%
1 67
 
10.0%
2 27
 
4.0%
3 25
 
3.7%
4 22
 
3.3%
7 21
 
3.1%
6 21
 
3.1%
8 14
 
2.1%
5 13
 
1.9%
9 12
 
1.8%
Other values (172) 271
40.3%
2023-12-12T09:19:33.866581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 229
21.4%
1 207
19.3%
2 110
10.3%
4 103
9.6%
3 97
9.0%
6 77
 
7.2%
7 66
 
6.2%
5 61
 
5.7%
9 61
 
5.7%
8 60
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1071
99.9%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 229
21.4%
1 207
19.3%
2 110
10.3%
4 103
9.6%
3 97
9.1%
6 77
 
7.2%
7 66
 
6.2%
5 61
 
5.7%
9 61
 
5.7%
8 60
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1072
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 229
21.4%
1 207
19.3%
2 110
10.3%
4 103
9.6%
3 97
9.0%
6 77
 
7.2%
7 66
 
6.2%
5 61
 
5.7%
9 61
 
5.7%
8 60
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1072
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 229
21.4%
1 207
19.3%
2 110
10.3%
4 103
9.6%
3 97
9.0%
6 77
 
7.2%
7 66
 
6.2%
5 61
 
5.7%
9 61
 
5.7%
8 60
 
5.6%

심하지않은장애인수(남성)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct157
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.698366
Minimum0
Maximum847
Zeros217
Zeros (%)32.2%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-12-12T09:19:34.021107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q336
95-th percentile285
Maximum847
Range847
Interquartile range (IQR)36

Descriptive statistics

Standard deviation98.460363
Coefficient of variation (CV)2.2027732
Kurtosis12.661562
Mean44.698366
Median Absolute Deviation (MAD)4
Skewness3.2376079
Sum30082
Variance9694.4431
MonotonicityNot monotonic
2023-12-12T09:19:34.162009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 217
32.2%
1 50
 
7.4%
2 35
 
5.2%
4 35
 
5.2%
3 33
 
4.9%
5 19
 
2.8%
6 18
 
2.7%
9 12
 
1.8%
7 10
 
1.5%
8 9
 
1.3%
Other values (147) 235
34.9%
ValueCountFrequency (%)
0 217
32.2%
1 50
 
7.4%
2 35
 
5.2%
3 33
 
4.9%
4 35
 
5.2%
5 19
 
2.8%
6 18
 
2.7%
7 10
 
1.5%
8 9
 
1.3%
9 12
 
1.8%
ValueCountFrequency (%)
847 1
0.1%
575 1
0.1%
514 1
0.1%
503 1
0.1%
484 1
0.1%
463 1
0.1%
446 1
0.1%
433 1
0.1%
410 1
0.1%
403 1
0.1%

심하지않은장애인수(여성)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct134
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.716196
Minimum0
Maximum652
Zeros254
Zeros (%)37.7%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-12-12T09:19:34.358998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q320
95-th percentile214.4
Maximum652
Range652
Interquartile range (IQR)20

Descriptive statistics

Standard deviation77.777124
Coefficient of variation (CV)2.24037
Kurtosis11.963165
Mean34.716196
Median Absolute Deviation (MAD)2
Skewness3.17157
Sum23364
Variance6049.2809
MonotonicityNot monotonic
2023-12-12T09:19:34.490976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 254
37.7%
1 75
 
11.1%
2 37
 
5.5%
3 30
 
4.5%
4 18
 
2.7%
5 14
 
2.1%
17 11
 
1.6%
15 10
 
1.5%
6 10
 
1.5%
7 7
 
1.0%
Other values (124) 207
30.8%
ValueCountFrequency (%)
0 254
37.7%
1 75
 
11.1%
2 37
 
5.5%
3 30
 
4.5%
4 18
 
2.7%
5 14
 
2.1%
6 10
 
1.5%
7 7
 
1.0%
8 5
 
0.7%
9 5
 
0.7%
ValueCountFrequency (%)
652 1
0.1%
425 1
0.1%
424 1
0.1%
406 1
0.1%
385 1
0.1%
361 1
0.1%
360 1
0.1%
351 1
0.1%
346 1
0.1%
329 1
0.1%

Interactions

2023-12-12T09:19:27.922163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:24.953300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:25.496335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:26.068334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:26.597889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:27.248876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:28.042139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:25.031307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:25.582206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:26.164596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:26.685873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:27.385158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:28.205763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:25.133112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:25.684067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:26.257106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:26.794009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:27.504656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:28.315960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:25.230027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:25.780256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:26.335376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:26.901578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:27.601500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:28.416907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:25.335943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:25.886470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:26.421289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:27.014406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:27.691946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:28.521845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:25.411811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:25.976874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:26.499236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:27.122651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:19:27.813539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:19:34.589102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구명읍면동명장애유형등록장애인수(여성)심한장애인수(합계)심한장애인수(남성)심한장애인수(여성)심하지않은장애인수(남성)심하지않은장애인수(여성)
구명1.0001.0000.0000.0000.0000.0000.0000.0380.000
읍면동명1.0001.0000.0000.0000.0000.0000.0000.0000.000
장애유형0.0000.0001.0000.7280.6520.6700.6430.8050.805
등록장애인수(여성)0.0000.0000.7281.0000.8970.8690.8770.9040.932
심한장애인수(합계)0.0000.0000.6520.8971.0000.9880.9790.8560.872
심한장애인수(남성)0.0000.0000.6700.8690.9881.0000.9540.8470.848
심한장애인수(여성)0.0000.0000.6430.8770.9790.9541.0000.8190.833
심하지않은장애인수(남성)0.0380.0000.8050.9040.8560.8470.8191.0000.980
심하지않은장애인수(여성)0.0000.0000.8050.9320.8720.8480.8330.9801.000
2023-12-12T09:19:34.712152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동명장애유형구명
읍면동명1.0000.0000.969
장애유형0.0001.0000.000
구명0.9690.0001.000
2023-12-12T09:19:34.813689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록장애인수(여성)심한장애인수(합계)심한장애인수(남성)심한장애인수(여성)심하지않은장애인수(남성)심하지않은장애인수(여성)구명읍면동명장애유형
등록장애인수(여성)1.0000.8520.8020.9050.6890.7590.0000.0000.405
심한장애인수(합계)0.8521.0000.9840.9560.4020.4380.0000.0000.321
심한장애인수(남성)0.8020.9841.0000.9000.3720.4070.0000.0000.336
심한장애인수(여성)0.9050.9560.9001.0000.4600.4940.0000.0000.314
심하지않은장애인수(남성)0.6890.4020.3720.4601.0000.9130.0230.0000.418
심하지않은장애인수(여성)0.7590.4380.4070.4940.9131.0000.0000.0000.418
구명0.0000.0000.0000.0000.0230.0001.0000.9690.000
읍면동명0.0000.0000.0000.0000.0000.0000.9691.0000.000
장애유형0.4050.3210.3360.3140.4180.4180.0000.0001.000

Missing values

2023-12-12T09:19:28.679749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:19:28.914890image/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

시군명구명읍면동명장애유형등록장애인수(합계)등록장애인수(남성)등록장애인수(여성)심한장애인수(합계)심한장애인수(남성)심한장애인수(여성)심하지않은장애인수(합계)심하지않은장애인수(남성)심하지않은장애인수(여성)
0고양시덕양구주교동소계67239827423714592435253182
1고양시덕양구주교동지체301166135433013258136122
2고양시덕양구주교동시각7550251358624517
3고양시덕양구주교동청각935241221111714130
4고양시덕양구주교동언어752422330
5고양시덕양구주교동지적483117483117000
6고양시덕양구주교동뇌병변51321931191220137
7고양시덕양구주교동자폐성1312113121000
8고양시덕양구주교동정신261115241014211
9고양시덕양구주교동신장342014301713431
시군명구명읍면동명장애유형등록장애인수(합계)등록장애인수(남성)등록장애인수(여성)심한장애인수(합계)심한장애인수(남성)심한장애인수(여성)심하지않은장애인수(합계)심하지않은장애인수(남성)심하지않은장애인수(여성)
663고양시일산서구가좌동지적574017574017000
664고양시일산서구가좌동뇌병변733736492722241014
665고양시일산서구가좌동자폐성2424024240000
666고양시일산서구가좌동정신12931293000
667고양시일산서구가좌동신장311714231310844
668고양시일산서구가좌동심장101000101
669고양시일산서구가좌동호흡기532532000
670고양시일산서구가좌동간523000523
671고양시일산서구가좌동장루.요루321101220
672고양시일산서구가좌동뇌전증101101000