Overview

Dataset statistics

Number of variables7
Number of observations26
Missing cells7
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory65.1 B

Variable types

Text3
Numeric4

Dataset

Description인천광역시 서구의 동별 등록장애인 현황에 관한 데이터입니다. 관내 동별 장애인 수, 장애정도별 장애인 수에 대한 데이터를 제공합니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15120980&srcSe=7661IVAWM27C61E190

Alerts

심한 장애_남성 is highly overall correlated with 심한 장애_여성 and 2 other fieldsHigh correlation
심한 장애_여성 is highly overall correlated with 심한 장애_남성 and 2 other fieldsHigh correlation
심하지 않은 장애_남성 is highly overall correlated with 심한 장애_남성 and 2 other fieldsHigh correlation
심하지 않은 장애_여성 is highly overall correlated with 심한 장애_남성 and 2 other fieldsHigh correlation
읍면동 has 1 (3.8%) missing valuesMissing
등록장애인수_남성 has 1 (3.8%) missing valuesMissing
등록장애인수_여성 has 1 (3.8%) missing valuesMissing
심한 장애_남성 has 1 (3.8%) missing valuesMissing
심한 장애_여성 has 1 (3.8%) missing valuesMissing
심하지 않은 장애_남성 has 1 (3.8%) missing valuesMissing
심하지 않은 장애_여성 has 1 (3.8%) missing valuesMissing
심한 장애_남성 has 1 (3.8%) zerosZeros
심한 장애_여성 has 2 (7.7%) zerosZeros
심하지 않은 장애_여성 has 2 (7.7%) zerosZeros

Reproduction

Analysis started2024-03-18 02:05:53.093001
Analysis finished2024-03-18 02:05:56.665808
Duration3.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동
Text

MISSING 

Distinct25
Distinct (%)100.0%
Missing1
Missing (%)3.8%
Memory size340.0 B
2024-03-18T11:05:56.789861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.92
Min length3

Characters and Unicode

Total characters98
Distinct characters37
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

Unique25 ?
Unique (%)100.0%

Sample

1st row검암경서동
2nd row연희동
3rd row청라1동
4th row청라2동
5th row청라3동
ValueCountFrequency (%)
검암경서동 1
 
4.0%
가좌2동 1
 
4.0%
마전동 1
 
4.0%
오류왕길동 1
 
4.0%
당하동 1
 
4.0%
원당동 1
 
4.0%
불로대곡동 1
 
4.0%
검단동 1
 
4.0%
검단5동 1
 
4.0%
검단4동 1
 
4.0%
Other values (15) 15
60.0%
2024-03-18T11:05:57.082481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 25
25.5%
가 7
 
7.1%
검 4
 
4.1%
좌 4
 
4.1%
라 4
 
4.1%
1 4
 
4.1%
2 4
 
4.1%
3 4
 
4.1%
남 3
 
3.1%
단 3
 
3.1%
Other values (27) 36
36.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83
84.7%
Decimal Number 15
 
15.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 25
30.1%
가 7
 
8.4%
검 4
 
4.8%
좌 4
 
4.8%
라 4
 
4.8%
남 3
 
3.6%
단 3
 
3.6%
청 3
 
3.6%
정 3
 
3.6%
석 3
 
3.6%
Other values (22) 24
28.9%
Decimal Number
ValueCountFrequency (%)
1 4
26.7%
2 4
26.7%
3 4
26.7%
4 2
13.3%
5 1
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83
84.7%
Common 15
 
15.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 25
30.1%
가 7
 
8.4%
검 4
 
4.8%
좌 4
 
4.8%
라 4
 
4.8%
남 3
 
3.6%
단 3
 
3.6%
청 3
 
3.6%
정 3
 
3.6%
석 3
 
3.6%
Other values (22) 24
28.9%
Common
ValueCountFrequency (%)
1 4
26.7%
2 4
26.7%
3 4
26.7%
4 2
13.3%
5 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83
84.7%
ASCII 15
 
15.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 25
30.1%
가 7
 
8.4%
검 4
 
4.8%
좌 4
 
4.8%
라 4
 
4.8%
남 3
 
3.6%
단 3
 
3.6%
청 3
 
3.6%
정 3
 
3.6%
석 3
 
3.6%
Other values (22) 24
28.9%
ASCII
ValueCountFrequency (%)
1 4
26.7%
2 4
26.7%
3 4
26.7%
4 2
13.3%
5 1
 
6.7%

등록장애인수_남성
Text

MISSING 

Distinct25
Distinct (%)100.0%
Missing1
Missing (%)3.8%
Memory size340.0 B
2024-03-18T11:05:57.271577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.08
Min length1

Characters and Unicode

Total characters77
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

Unique25 ?
Unique (%)100.0%

Sample

1st row1,067
2nd row1,520
3rd row417
4th row693
5th row463
ValueCountFrequency (%)
1,067 1
 
4.0%
548 1
 
4.0%
481 1
 
4.0%
745 1
 
4.0%
502 1
 
4.0%
476 1
 
4.0%
592 1
 
4.0%
1,008 1
 
4.0%
1 1
 
4.0%
2 1
 
4.0%
Other values (15) 15
60.0%
2024-03-18T11:05:57.557339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 9
11.7%
5 9
11.7%
7 8
10.4%
4 8
10.4%
6 7
9.1%
2 7
9.1%
9 7
9.1%
8 7
9.1%
0 6
7.8%
3 6
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74
96.1%
Other Punctuation 3
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9
12.2%
5 9
12.2%
7 8
10.8%
4 8
10.8%
6 7
9.5%
2 7
9.5%
9 7
9.5%
8 7
9.5%
0 6
8.1%
3 6
8.1%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 77
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9
11.7%
5 9
11.7%
7 8
10.4%
4 8
10.4%
6 7
9.1%
2 7
9.1%
9 7
9.1%
8 7
9.1%
0 6
7.8%
3 6
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9
11.7%
5 9
11.7%
7 8
10.4%
4 8
10.4%
6 7
9.1%
2 7
9.1%
9 7
9.1%
8 7
9.1%
0 6
7.8%
3 6
7.8%

등록장애인수_여성
Text

MISSING 

Distinct23
Distinct (%)92.0%
Missing1
Missing (%)3.8%
Memory size340.0 B
2024-03-18T11:05:57.716407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.92
Min length1

Characters and Unicode

Total characters73
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

Unique21 ?
Unique (%)84.0%

Sample

1st row665
2nd row1,089
3rd row260
4th row425
5th row261
ValueCountFrequency (%)
345 2
 
8.0%
0 2
 
8.0%
665 1
 
4.0%
463 1
 
4.0%
313 1
 
4.0%
455 1
 
4.0%
327 1
 
4.0%
373 1
 
4.0%
631 1
 
4.0%
218 1
 
4.0%
Other values (13) 13
52.0%
2024-03-18T11:05:57.973275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 12
16.4%
6 10
13.7%
5 9
12.3%
2 9
12.3%
4 7
9.6%
0 7
9.6%
1 7
9.6%
7 4
 
5.5%
9 4
 
5.5%
8 3
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72
98.6%
Other Punctuation 1
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 12
16.7%
6 10
13.9%
5 9
12.5%
2 9
12.5%
4 7
9.7%
0 7
9.7%
1 7
9.7%
7 4
 
5.6%
9 4
 
5.6%
8 3
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 12
16.4%
6 10
13.7%
5 9
12.3%
2 9
12.3%
4 7
9.6%
0 7
9.6%
1 7
9.6%
7 4
 
5.5%
9 4
 
5.5%
8 3
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 12
16.4%
6 10
13.7%
5 9
12.3%
2 9
12.3%
4 7
9.6%
0 7
9.6%
1 7
9.6%
7 4
 
5.5%
9 4
 
5.5%
8 3
 
4.1%

심한 장애_남성
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct24
Distinct (%)96.0%
Missing1
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean215.4
Minimum0
Maximum625
Zeros1
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-18T11:05:58.071783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.2
Q1157
median190
Q3279
95-th percentile362.6
Maximum625
Range625
Interquartile range (IQR)122

Descriptive statistics

Standard deviation128.75364
Coefficient of variation (CV)0.59774206
Kurtosis3.2654652
Mean215.4
Median Absolute Deviation (MAD)62
Skewness1.1218482
Sum5385
Variance16577.5
MonotonicityNot monotonic
2024-03-18T11:05:58.168315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
157 2
 
7.7%
363 1
 
3.8%
190 1
 
3.8%
279 1
 
3.8%
259 1
 
3.8%
182 1
 
3.8%
159 1
 
3.8%
209 1
 
3.8%
361 1
 
3.8%
0 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
0 1
3.8%
1 1
3.8%
77 1
3.8%
114 1
3.8%
128 1
3.8%
129 1
3.8%
157 2
7.7%
159 1
3.8%
181 1
3.8%
182 1
3.8%
ValueCountFrequency (%)
625 1
3.8%
363 1
3.8%
361 1
3.8%
333 1
3.8%
312 1
3.8%
295 1
3.8%
279 1
3.8%
259 1
3.8%
243 1
3.8%
231 1
3.8%

심한 장애_여성
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct24
Distinct (%)96.0%
Missing1
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean143.32
Minimum0
Maximum453
Zeros2
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-18T11:05:58.263141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.2
Q191
median125
Q3172
95-th percentile239.6
Maximum453
Range453
Interquartile range (IQR)81

Descriptive statistics

Standard deviation92.45662
Coefficient of variation (CV)0.6451062
Kurtosis4.2810005
Mean143.32
Median Absolute Deviation (MAD)45
Skewness1.4197946
Sum3583
Variance8548.2267
MonotonicityNot monotonic
2024-03-18T11:05:58.388268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 2
 
7.7%
238 1
 
3.8%
125 1
 
3.8%
164 1
 
3.8%
116 1
 
3.8%
168 1
 
3.8%
118 1
 
3.8%
117 1
 
3.8%
141 1
 
3.8%
240 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
0 2
7.7%
41 1
3.8%
72 1
3.8%
77 1
3.8%
80 1
3.8%
91 1
3.8%
111 1
3.8%
116 1
3.8%
117 1
3.8%
118 1
3.8%
ValueCountFrequency (%)
453 1
3.8%
240 1
3.8%
238 1
3.8%
236 1
3.8%
219 1
3.8%
198 1
3.8%
172 1
3.8%
168 1
3.8%
164 1
3.8%
148 1
3.8%

심하지 않은 장애_남성
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)96.0%
Missing1
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean403.88
Minimum1
Maximum895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-18T11:05:58.532580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile39.6
Q1306
median374
Q3498
95-th percentile692.6
Maximum895
Range894
Interquartile range (IQR)192

Descriptive statistics

Standard deviation207.56552
Coefficient of variation (CV)0.51392868
Kurtosis0.43061987
Mean403.88
Median Absolute Deviation (MAD)112
Skewness0.17968844
Sum10097
Variance43083.443
MonotonicityNot monotonic
2024-03-18T11:05:58.617791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 2
 
7.7%
704 1
 
3.8%
358 1
 
3.8%
482 1
 
3.8%
324 1
 
3.8%
486 1
 
3.8%
320 1
 
3.8%
317 1
 
3.8%
383 1
 
3.8%
647 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
1 2
7.7%
194 1
3.8%
214 1
3.8%
239 1
3.8%
289 1
3.8%
306 1
3.8%
317 1
3.8%
320 1
3.8%
324 1
3.8%
355 1
3.8%
ValueCountFrequency (%)
895 1
3.8%
704 1
3.8%
647 1
3.8%
630 1
3.8%
617 1
3.8%
605 1
3.8%
498 1
3.8%
486 1
3.8%
482 1
3.8%
462 1
3.8%

심하지 않은 장애_여성
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct24
Distinct (%)96.0%
Missing1
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean255.68
Minimum0
Maximum636
Zeros2
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-18T11:05:58.705387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.2
Q1181
median234
Q3328
95-th percentile424.4
Maximum636
Range636
Interquartile range (IQR)147

Descriptive statistics

Standard deviation138.32712
Coefficient of variation (CV)0.5410166
Kurtosis1.3893758
Mean255.68
Median Absolute Deviation (MAD)65
Skewness0.50685184
Sum6392
Variance19134.393
MonotonicityNot monotonic
2024-03-18T11:05:58.823480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 2
 
7.7%
427 1
 
3.8%
267 1
 
3.8%
299 1
 
3.8%
197 1
 
3.8%
287 1
 
3.8%
227 1
 
3.8%
210 1
 
3.8%
232 1
 
3.8%
391 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
0 2
7.7%
106 1
3.8%
136 1
3.8%
141 1
3.8%
169 1
3.8%
181 1
3.8%
197 1
3.8%
210 1
3.8%
224 1
3.8%
227 1
3.8%
ValueCountFrequency (%)
636 1
3.8%
427 1
3.8%
414 1
3.8%
402 1
3.8%
391 1
3.8%
354 1
3.8%
328 1
3.8%
299 1
3.8%
287 1
3.8%
277 1
3.8%

Interactions

2024-03-18T11:05:55.956273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:05:54.884612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:05:55.334404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:05:55.702317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:05:56.020565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:05:55.028669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:05:55.452798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:05:55.764595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:05:56.149964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:05:55.133234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:05:55.562959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:05:55.824877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:05:56.266305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:05:55.224860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:05:55.627407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:05:55.891762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T11:05:58.908085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동등록장애인수_남성등록장애인수_여성심한 장애_남성심한 장애_여성심하지 않은 장애_남성심하지 않은 장애_여성
읍면동1.0001.0001.0001.0001.0001.0001.000
등록장애인수_남성1.0001.0001.0001.0001.0001.0001.000
등록장애인수_여성1.0001.0001.0000.9741.0000.9691.000
심한 장애_남성1.0001.0000.9741.0000.9710.8710.874
심한 장애_여성1.0001.0001.0000.9711.0000.8630.873
심하지 않은 장애_남성1.0001.0000.9690.8710.8631.0000.939
심하지 않은 장애_여성1.0001.0001.0000.8740.8730.9391.000
2024-03-18T11:05:59.015804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
심한 장애_남성심한 장애_여성심하지 않은 장애_남성심하지 않은 장애_여성
심한 장애_남성1.0000.9670.9760.973
심한 장애_여성0.9671.0000.9670.953
심하지 않은 장애_남성0.9760.9671.0000.981
심하지 않은 장애_여성0.9730.9530.9811.000

Missing values

2024-03-18T11:05:56.374260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T11:05:56.472845image/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.
2024-03-18T11:05:56.574141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

읍면동등록장애인수_남성등록장애인수_여성심한 장애_남성심한 장애_여성심하지 않은 장애_남성심하지 않은 장애_여성
0검암경서동1,067665363238704427
1연희동1,5201,089625453895636
2청라1동41726012891289169
3청라2동693425231172462253
4청라3동46326115780306181
5가정1동938650333236605414
6가정2동2911477741214106
7가정3동30820811472194136
8석남1동925552295198630354
9석남2동543360188136355224
읍면동등록장애인수_남성등록장애인수_여성심한 장애_남성심한 장애_여성심하지 않은 장애_남성심하지 않은 장애_여성
16검단4동201010
17검단5동100010
18검단동1,008631361240647391
19불로대곡동592373209141383232
20원당동476327159117317210
21당하동502345182118320227
22오류왕길동745455259168486287
23마전동481313157116324197
24아라동761463279164482299
25<NA><NA><NA><NA><NA><NA><NA>