Overview

Dataset statistics

Number of variables12
Number of observations31
Missing cells47
Missing cells (%)12.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory107.3 B

Variable types

Text4
Numeric4
Categorical4

Dataset

Description장애인 심부름센터 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=6106JA7895CUYGPI972C1264277&infSeq=1

Alerts

영업상태명 has constant value ""Constant
소재지우편번호 is highly overall correlated with WGS84위도High correlation
WGS84위도 is highly overall correlated with 소재지우편번호High correlation
입소정원(명) is highly imbalanced (53.5%)Imbalance
소재지도로명주소 has 13 (41.9%) missing valuesMissing
소재지우편번호 has 10 (32.3%) missing valuesMissing
WGS84위도 has 12 (38.7%) missing valuesMissing
WGS84경도 has 12 (38.7%) missing valuesMissing
사업장명 has unique valuesUnique
소재지지번주소 has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:31:08.195760
Analysis finished2023-12-10 22:31:11.117254
Duration2.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T07:31:11.233840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0967742
Min length3

Characters and Unicode

Total characters96
Distinct characters36
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

Unique29 ?
Unique (%)93.5%

Sample

1st row가평군
2nd row고양시
3rd row과천시
4th row광명시
5th row광주시
ValueCountFrequency (%)
군포시 2
 
6.5%
가평군 1
 
3.2%
양주시 1
 
3.2%
하남시 1
 
3.2%
포천시 1
 
3.2%
평택시 1
 
3.2%
파주시 1
 
3.2%
이천시 1
 
3.2%
의정부시 1
 
3.2%
의왕시 1
 
3.2%
Other values (20) 20
64.5%
2023-12-11T07:31:11.518436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
군 5
 
5.2%
주 5
 
5.2%
양 5
 
5.2%
포 4
 
4.2%
안 3
 
3.1%
남 3
 
3.1%
성 3
 
3.1%
평 3
 
3.1%
Other values (26) 30
31.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 96
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
군 5
 
5.2%
주 5
 
5.2%
양 5
 
5.2%
포 4
 
4.2%
안 3
 
3.1%
남 3
 
3.1%
성 3
 
3.1%
평 3
 
3.1%
Other values (26) 30
31.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
군 5
 
5.2%
주 5
 
5.2%
양 5
 
5.2%
포 4
 
4.2%
안 3
 
3.1%
남 3
 
3.1%
성 3
 
3.1%
평 3
 
3.1%
Other values (26) 30
31.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
군 5
 
5.2%
주 5
 
5.2%
양 5
 
5.2%
포 4
 
4.2%
안 3
 
3.1%
남 3
 
3.1%
성 3
 
3.1%
평 3
 
3.1%
Other values (26) 30
31.2%

사업장명
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T07:31:11.721246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length14.612903
Min length13

Characters and Unicode

Total characters453
Distinct characters48
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

Unique31 ?
Unique (%)100.0%

Sample

1st row가평군 장애인생활이동지원센터
2nd row고양시 장애인 생활이동지원센터
3rd row과천시장애인생활이동지원센터
4th row광명시장애인생활이동지원센터
5th row광주시 장애인생활이동지원센터
ValueCountFrequency (%)
장애인생활이동지원센터 9
 
18.0%
장애인 4
 
8.0%
생활이동지원센터 4
 
8.0%
군포시 2
 
4.0%
의왕시 1
 
2.0%
양평군장애인생활이동지원센터 1
 
2.0%
여주시장애인생활이동지원센터 1
 
2.0%
연천군 1
 
2.0%
오산시 1
 
2.0%
용인시 1
 
2.0%
Other values (25) 25
50.0%
2023-12-11T07:31:12.079675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 32
 
7.1%
터 31
 
6.8%
센 31
 
6.8%
지 31
 
6.8%
이 31
 
6.8%
동 31
 
6.8%
애 31
 
6.8%
장 31
 
6.8%
생 30
 
6.6%
활 30
 
6.6%
Other values (38) 144
31.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 434
95.8%
Space Separator 19
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 32
 
7.4%
터 31
 
7.1%
센 31
 
7.1%
지 31
 
7.1%
이 31
 
7.1%
동 31
 
7.1%
애 31
 
7.1%
장 31
 
7.1%
생 30
 
6.9%
활 30
 
6.9%
Other values (37) 125
28.8%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 434
95.8%
Common 19
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 32
 
7.4%
터 31
 
7.1%
센 31
 
7.1%
지 31
 
7.1%
이 31
 
7.1%
동 31
 
7.1%
애 31
 
7.1%
장 31
 
7.1%
생 30
 
6.9%
활 30
 
6.9%
Other values (37) 125
28.8%
Common
ValueCountFrequency (%)
19
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 434
95.8%
ASCII 19
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 32
 
7.4%
터 31
 
7.1%
센 31
 
7.1%
지 31
 
7.1%
이 31
 
7.1%
동 31
 
7.1%
애 31
 
7.1%
장 31
 
7.1%
생 30
 
6.9%
활 30
 
6.9%
Other values (37) 125
28.8%
ASCII
ValueCountFrequency (%)
19
100.0%

인허가일자
Real number (ℝ)

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20053221
Minimum20010619
Maximum20090227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:31:12.201101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010619
5-th percentile20020859
Q120040716
median20051128
Q320061108
95-th percentile20080126
Maximum20090227
Range79608
Interquartile range (IQR)20392.5

Descriptive statistics

Standard deviation18504.372
Coefficient of variation (CV)0.00092276308
Kurtosis0.065585886
Mean20053221
Median Absolute Deviation (MAD)10002
Skewness-0.29633118
Sum6.2164986 × 108
Variance3.4241179 × 108
MonotonicityNot monotonic
2023-12-11T07:31:12.314392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20051011 2
 
6.5%
20080123 1
 
3.2%
20040224 1
 
3.2%
20061004 1
 
3.2%
20070501 1
 
3.2%
20031001 1
 
3.2%
20060302 1
 
3.2%
20061101 1
 
3.2%
20010619 1
 
3.2%
20061115 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
20010619 1
3.2%
20020807 1
3.2%
20020911 1
3.2%
20030528 1
3.2%
20031001 1
3.2%
20040224 1
3.2%
20040227 1
3.2%
20040305 1
3.2%
20041126 1
3.2%
20050120 1
3.2%
ValueCountFrequency (%)
20090227 1
3.2%
20080128 1
3.2%
20080123 1
3.2%
20080115 1
3.2%
20070720 1
3.2%
20070501 1
3.2%
20061219 1
3.2%
20061115 1
3.2%
20061101 1
3.2%
20061004 1
3.2%

영업상태명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
운영중
31 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 31
100.0%

Length

2023-12-11T07:31:12.452449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:31:12.536601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 31
100.0%

입소정원(명)
Categorical

IMBALANCE 

Distinct5
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size380.0 B
0
25 
1
 
2
7
 
2
20
 
1
13180
 
1

Length

Max length5
Median length1
Mean length1.1612903
Min length1

Unique

Unique2 ?
Unique (%)6.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 25
80.6%
1 2
 
6.5%
7 2
 
6.5%
20 1
 
3.2%
13180 1
 
3.2%

Length

2023-12-11T07:31:12.640351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:31:12.748033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25
80.6%
1 2
 
6.5%
7 2
 
6.5%
20 1
 
3.2%
13180 1
 
3.2%

자격소유인원수(명)
Categorical

Distinct5
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size380.0 B
0
12 
1
11 
2
4 
3
3 
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0967742
Min length1

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row0
2nd row1
3rd row<NA>
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 12
38.7%
1 11
35.5%
2 4
 
12.9%
3 3
 
9.7%
<NA> 1
 
3.2%

Length

2023-12-11T07:31:12.867240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:31:12.998603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12
38.7%
1 11
35.5%
2 4
 
12.9%
3 3
 
9.7%
na 1
 
3.2%

총인원수(명)
Categorical

Distinct4
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size380.0 B
6
14 
5
9 
3
7 
40
 
1

Length

Max length2
Median length1
Mean length1.0322581
Min length1

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row3
2nd row6
3rd row40
4th row5
5th row6

Common Values

ValueCountFrequency (%)
6 14
45.2%
5 9
29.0%
3 7
22.6%
40 1
 
3.2%

Length

2023-12-11T07:31:13.113889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:31:13.233608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6 14
45.2%
5 9
29.0%
3 7
22.6%
40 1
 
3.2%

소재지도로명주소
Text

MISSING 

Distinct18
Distinct (%)100.0%
Missing13
Missing (%)41.9%
Memory size380.0 B
2023-12-11T07:31:13.414402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length17.777778
Min length14

Characters and Unicode

Total characters320
Distinct characters68
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)100.0%

Sample

1st row경기도 가평군 가평읍 비석거리길 4-11
2nd row경기도 광명시 금당로 47
3rd row경기도 군포시 산본로 329
4th row경기도 군포시 산본로323번길 4-17
5th row경기도 남양주시 경춘로 1298
ValueCountFrequency (%)
경기도 18
 
23.4%
군포시 2
 
2.6%
38 2
 
2.6%
11 2
 
2.6%
문화로 2
 
2.6%
광명시 1
 
1.3%
이천시 1
 
1.3%
4-11 1
 
1.3%
오산시 1
 
1.3%
경기동로 1
 
1.3%
Other values (46) 46
59.7%
2023-12-11T07:31:13.744906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
59
18.4%
경 21
 
6.6%
기 19
 
5.9%
도 18
 
5.6%
1 17
 
5.3%
시 16
 
5.0%
로 15
 
4.7%
길 9
 
2.8%
4 9
 
2.8%
천 7
 
2.2%
Other values (58) 130
40.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 200
62.5%
Space Separator 59
 
18.4%
Decimal Number 58
 
18.1%
Dash Punctuation 3
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 21
 
10.5%
기 19
 
9.5%
도 18
 
9.0%
시 16
 
8.0%
로 15
 
7.5%
길 9
 
4.5%
천 7
 
3.5%
번 6
 
3.0%
남 5
 
2.5%
주 4
 
2.0%
Other values (46) 80
40.0%
Decimal Number
ValueCountFrequency (%)
1 17
29.3%
4 9
15.5%
3 7
12.1%
7 6
 
10.3%
5 4
 
6.9%
9 4
 
6.9%
8 4
 
6.9%
2 4
 
6.9%
0 2
 
3.4%
6 1
 
1.7%
Space Separator
ValueCountFrequency (%)
59
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 200
62.5%
Common 120
37.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 21
 
10.5%
기 19
 
9.5%
도 18
 
9.0%
시 16
 
8.0%
로 15
 
7.5%
길 9
 
4.5%
천 7
 
3.5%
번 6
 
3.0%
남 5
 
2.5%
주 4
 
2.0%
Other values (46) 80
40.0%
Common
ValueCountFrequency (%)
59
49.2%
1 17
 
14.2%
4 9
 
7.5%
3 7
 
5.8%
7 6
 
5.0%
5 4
 
3.3%
9 4
 
3.3%
8 4
 
3.3%
2 4
 
3.3%
- 3
 
2.5%
Other values (2) 3
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 200
62.5%
ASCII 120
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
59
49.2%
1 17
 
14.2%
4 9
 
7.5%
3 7
 
5.8%
7 6
 
5.0%
5 4
 
3.3%
9 4
 
3.3%
8 4
 
3.3%
2 4
 
3.3%
- 3
 
2.5%
Other values (2) 3
 
2.5%
Hangul
ValueCountFrequency (%)
경 21
 
10.5%
기 19
 
9.5%
도 18
 
9.0%
시 16
 
8.0%
로 15
 
7.5%
길 9
 
4.5%
천 7
 
3.5%
번 6
 
3.0%
남 5
 
2.5%
주 4
 
2.0%
Other values (46) 80
40.0%

소재지지번주소
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T07:31:13.972688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length26
Mean length19.806452
Min length10

Characters and Unicode

Total characters614
Distinct characters111
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row경기도 가평군 가평읍 대곡리 171-3번지
2nd row경기도 고양시 덕양구 성사동 369-3번지
3rd row경기도 과천시 문원동
4th row경기도 광명시 하안동 110-7번지
5th row경기도 광주시 곤지암읍 신대리
ValueCountFrequency (%)
경기도 31
 
22.1%
산본동 2
 
1.4%
군포시 2
 
1.4%
오전동 1
 
0.7%
19호 1
 
0.7%
종합운동장내 1
 
0.7%
마평동 1
 
0.7%
처인구 1
 
0.7%
용인시 1
 
0.7%
49번지 1
 
0.7%
Other values (98) 98
70.0%
2023-12-11T07:31:14.335353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
109
 
17.8%
경 32
 
5.2%
도 32
 
5.2%
기 31
 
5.0%
동 29
 
4.7%
시 29
 
4.7%
지 22
 
3.6%
1 21
 
3.4%
번 19
 
3.1%
3 14
 
2.3%
Other values (101) 276
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 406
66.1%
Space Separator 109
 
17.8%
Decimal Number 86
 
14.0%
Dash Punctuation 13
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 32
 
7.9%
도 32
 
7.9%
기 31
 
7.6%
동 29
 
7.1%
시 29
 
7.1%
지 22
 
5.4%
번 19
 
4.7%
산 8
 
2.0%
양 8
 
2.0%
구 8
 
2.0%
Other values (89) 188
46.3%
Decimal Number
ValueCountFrequency (%)
1 21
24.4%
3 14
16.3%
0 10
11.6%
2 8
 
9.3%
7 7
 
8.1%
4 6
 
7.0%
6 6
 
7.0%
9 6
 
7.0%
8 5
 
5.8%
5 3
 
3.5%
Space Separator
ValueCountFrequency (%)
109
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 406
66.1%
Common 208
33.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 32
 
7.9%
도 32
 
7.9%
기 31
 
7.6%
동 29
 
7.1%
시 29
 
7.1%
지 22
 
5.4%
번 19
 
4.7%
산 8
 
2.0%
양 8
 
2.0%
구 8
 
2.0%
Other values (89) 188
46.3%
Common
ValueCountFrequency (%)
109
52.4%
1 21
 
10.1%
3 14
 
6.7%
- 13
 
6.2%
0 10
 
4.8%
2 8
 
3.8%
7 7
 
3.4%
4 6
 
2.9%
6 6
 
2.9%
9 6
 
2.9%
Other values (2) 8
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 406
66.1%
ASCII 208
33.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
109
52.4%
1 21
 
10.1%
3 14
 
6.7%
- 13
 
6.2%
0 10
 
4.8%
2 8
 
3.8%
7 7
 
3.4%
4 6
 
2.9%
6 6
 
2.9%
9 6
 
2.9%
Other values (2) 8
 
3.8%
Hangul
ValueCountFrequency (%)
경 32
 
7.9%
도 32
 
7.9%
기 31
 
7.6%
동 29
 
7.1%
시 29
 
7.1%
지 22
 
5.4%
번 19
 
4.7%
산 8
 
2.0%
양 8
 
2.0%
구 8
 
2.0%
Other values (89) 188
46.3%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)95.2%
Missing10
Missing (%)32.3%
Infinite0
Infinite (%)0.0%
Mean13631.19
Minimum10099
Maximum18131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:31:14.442748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10099
5-th percentile10463
Q111310
median13024
Q315865
95-th percentile17877
Maximum18131
Range8032
Interquartile range (IQR)4555

Descriptive statistics

Standard deviation2693.7706
Coefficient of variation (CV)0.19761814
Kurtosis-1.2919772
Mean13631.19
Median Absolute Deviation (MAD)2012
Skewness0.43952183
Sum286255
Variance7256399.9
MonotonicityNot monotonic
2023-12-11T07:31:14.542294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
15865 2
 
6.5%
11012 1
 
3.2%
13024 1
 
3.2%
11147 1
 
3.2%
17877 1
 
3.2%
10923 1
 
3.2%
17375 1
 
3.2%
11652 1
 
3.2%
16059 1
 
3.2%
18131 1
 
3.2%
Other values (10) 10
32.3%
(Missing) 10
32.3%
ValueCountFrequency (%)
10099 1
3.2%
10463 1
3.2%
10923 1
3.2%
11012 1
3.2%
11147 1
3.2%
11310 1
3.2%
11508 1
3.2%
11652 1
3.2%
12218 1
3.2%
12420 1
3.2%
ValueCountFrequency (%)
18131 1
3.2%
17877 1
3.2%
17590 1
3.2%
17375 1
3.2%
16059 1
3.2%
15865 2
6.5%
14306 1
3.2%
14092 1
3.2%
13319 1
3.2%
13024 1
3.2%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)100.0%
Missing12
Missing (%)38.7%
Infinite0
Infinite (%)0.0%
Mean37.560991
Minimum37.006854
Maximum38.107123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:31:14.656786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.006854
5-th percentile37.141859
Q137.360014
median37.523677
Q337.777894
95-th percentile37.93477
Maximum38.107123
Range1.1002685
Interquartile range (IQR)0.41787953

Descriptive statistics

Standard deviation0.28661852
Coefficient of variation (CV)0.0076307496
Kurtosis-0.56552934
Mean37.560991
Median Absolute Deviation (MAD)0.20982343
Skewness-0.0045793474
Sum713.65883
Variance0.082150175
MonotonicityNot monotonic
2023-12-11T07:31:14.763794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
37.6564678805 1
 
3.2%
37.5236772545 1
 
3.2%
37.895239847 1
 
3.2%
37.7559803251 1
 
3.2%
37.2786206219 1
 
3.2%
37.7335006848 1
 
3.2%
37.3512340607 1
 
3.2%
37.1568594226 1
 
3.2%
38.1071229181 1
 
3.2%
37.82374914 1
 
3.2%
Other values (9) 9
29.0%
(Missing) 12
38.7%
ValueCountFrequency (%)
37.0068544283 1
3.2%
37.1568594226 1
3.2%
37.2786206219 1
3.2%
37.3512340607 1
3.2%
37.36001327 1
3.2%
37.3600157065 1
3.2%
37.3854713903 1
3.2%
37.4390327828 1
3.2%
37.4600884387 1
3.2%
37.5236772545 1
3.2%
ValueCountFrequency (%)
38.1071229181 1
3.2%
37.915620114 1
3.2%
37.895239847 1
3.2%
37.82374914 1
3.2%
37.799807712 1
3.2%
37.7559803251 1
3.2%
37.7335006848 1
3.2%
37.6564678805 1
3.2%
37.6494731109 1
3.2%
37.5236772545 1
3.2%

WGS84경도
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)100.0%
Missing12
Missing (%)38.7%
Infinite0
Infinite (%)0.0%
Mean127.08134
Minimum126.77865
Maximum127.51466
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:31:14.876500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.77865
5-th percentile126.83207
Q1126.93283
median127.06049
Q3127.21215
95-th percentile127.44845
Maximum127.51466
Range0.73600442
Interquartile range (IQR)0.27931817

Descriptive statistics

Standard deviation0.19647186
Coefficient of variation (CV)0.0015460323
Kurtosis0.063957341
Mean127.08134
Median Absolute Deviation (MAD)0.13283572
Skewness0.67016226
Sum2414.5455
Variance0.038601191
MonotonicityNot monotonic
2023-12-11T07:31:14.989273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
126.8380056207 1
 
3.2%
127.2237168768 1
 
3.2%
127.2005875168 1
 
3.2%
126.7786535274 1
 
3.2%
127.4410944649 1
 
3.2%
127.0460443696 1
 
3.2%
126.9735645868 1
 
3.2%
127.0726136232 1
 
3.2%
127.0797747877 1
 
3.2%
127.5146579497 1
 
3.2%
Other values (9) 9
29.0%
(Missing) 12
38.7%
ValueCountFrequency (%)
126.7786535274 1
3.2%
126.8380056207 1
3.2%
126.8832484159 1
3.2%
126.9276550998 1
3.2%
126.9325799627 1
3.2%
126.9330880975 1
3.2%
126.9735645868 1
3.2%
126.9965113738 1
3.2%
127.0460443696 1
3.2%
127.0604908235 1
3.2%
ValueCountFrequency (%)
127.5146579497 1
3.2%
127.4410944649 1
3.2%
127.2768976973 1
3.2%
127.2370135497 1
3.2%
127.2237168768 1
3.2%
127.2005875168 1
3.2%
127.1292647901 1
3.2%
127.0797747877 1
3.2%
127.0726136232 1
3.2%
127.0604908235 1
3.2%

Interactions

2023-12-11T07:31:10.313976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:09.000917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:09.492383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:09.953091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:10.428769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:09.138602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:09.622302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:10.066136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:10.527875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:09.264204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:09.727790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:10.149414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:10.634668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:09.379206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:09.832507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:10.227180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:31:15.306988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업장명인허가일자입소정원(명)자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
시군명1.0001.0000.9131.0000.0000.8771.0001.0001.0001.0001.000
사업장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
인허가일자0.9131.0001.0000.0000.3660.4361.0001.0000.0000.6060.331
입소정원(명)1.0001.0000.0001.0000.0780.0001.0001.0000.3970.0000.000
자격소유인원수(명)0.0001.0000.3660.0781.0000.3081.0001.0000.0000.0000.346
총인원수(명)0.8771.0000.4360.0000.3081.0001.0001.0000.3100.5860.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지우편번호1.0001.0000.0000.3970.0000.3101.0001.0001.0000.6740.783
WGS84위도1.0001.0000.6060.0000.0000.5861.0001.0000.6741.0000.676
WGS84경도1.0001.0000.3310.0000.3460.0001.0001.0000.7830.6761.000
2023-12-11T07:31:15.431769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총인원수(명)자격소유인원수(명)입소정원(명)
총인원수(명)1.0000.2850.000
자격소유인원수(명)0.2851.0000.000
입소정원(명)0.0000.0001.000
2023-12-11T07:31:15.539538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자소재지우편번호WGS84위도WGS84경도입소정원(명)자격소유인원수(명)총인원수(명)
인허가일자1.000-0.2920.4960.3790.0000.3080.235
소재지우편번호-0.2921.000-0.9000.2260.1660.0000.000
WGS84위도0.496-0.9001.000-0.0140.0000.0000.282
WGS84경도0.3790.226-0.0141.0000.0000.0000.000
입소정원(명)0.0000.1660.0000.0001.0000.0000.000
자격소유인원수(명)0.3080.0000.0000.0000.0001.0000.285
총인원수(명)0.2350.0000.2820.0000.0000.2851.000

Missing values

2023-12-11T07:31:10.765841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:31:10.933107image/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.
2023-12-11T07:31:11.045535image/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

시군명사업장명인허가일자영업상태명입소정원(명)자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군가평군 장애인생활이동지원센터20080123운영중003경기도 가평군 가평읍 비석거리길 4-11경기도 가평군 가평읍 대곡리 171-3번지1242037.823749127.514658
1고양시고양시 장애인 생활이동지원센터20040227운영중016<NA>경기도 고양시 덕양구 성사동 369-3번지1046337.656468126.838006
2과천시과천시장애인생활이동지원센터20080115운영중0<NA>40<NA>경기도 과천시 문원동<NA><NA><NA>
3광명시광명시장애인생활이동지원센터20041126운영중015경기도 광명시 금당로 47경기도 광명시 하안동 110-7번지1430637.460088126.883248
4광주시광주시 장애인생활이동지원센터20051011운영중116<NA>경기도 광주시 곤지암읍 신대리<NA><NA><NA>
5구리시구리시장애인생활이동지원센터20060913운영중026<NA>경기도 구리시 교문동<NA><NA><NA>
6군포시군포시 장애인생활이동지원센터20060401운영중036경기도 군포시 산본로 329경기도 군포시 산본동 1126번지 경원빌딩 8층1586537.360016126.933088
7군포시군포시 지체장애인 심부름센터20020807운영중003경기도 군포시 산본로323번길 4-17경기도 군포시 산본동 1130번지 602호1586537.360013126.93258
8김포시김포시 장애인생활이동지원센터20050928운영중015<NA>경기도 김포시 걸포동10099<NA><NA>
9남양주시남양주장애인생활이동지원센터20060201운영중006경기도 남양주시 경춘로 1298경기도 남양주시 평내동 203-2번지 313호1221837.649473127.237014
시군명사업장명인허가일자영업상태명입소정원(명)자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
21오산시오산시 장애인 생활이동지원센터20050120운영중006경기도 오산시 경기동로 15경기도 오산시 오산동 49번지1813137.156859127.072614
22용인시용인시 장애인생활이동지원센터20050207운영중006<NA>경기도 용인시 처인구 마평동 종합운동장내 19호<NA><NA><NA>
23의왕시의왕시 장애인생활이동지원센터20061115운영중013경기도 의왕시 전주남이1길 7경기도 의왕시 오전동 347-1번지1605937.351234126.973565
24의정부시의정부시장애인생활이동지원센터20010619운영중035경기도 의정부시 경의로85번길 6-19경기도 의정부시 의정부동 580-6번지1165237.733501127.046044
25이천시이천시 장애인생활이동지원센터20061101운영중015경기도 이천시 중리천로34번길 38경기도 이천시 중리동 173-6번지1737537.278621127.441094
26파주시파주시 장애인 생활이동지원센터20060302운영중716경기도 파주시 문화로 13경기도 파주시 금촌동 791-3번지1092337.75598126.778654
27평택시평택시장애인생활이동지원센터20031001운영중016<NA>경기도 평택시 합정동17877<NA><NA>
28포천시포천시 장애인생활이동지원센터20070501운영중003경기도 포천시 신읍길 11경기도 포천시 신읍동 58-14번지1114737.89524127.200588
29하남시하남시장애인생활이동지원센터20061004운영중006경기도 하남시 검단로19번길 27경기도 하남시 하산곡동 69-1번지1302437.523677127.223717
30화성시화성시장애인생활이동지원센터20051011운영중706<NA>경기도 화성시 향남읍 도이리 아르딤복지관 장애인단체 및 센터동 101호<NA><NA><NA>