Overview

Dataset statistics

Number of variables12
Number of observations35
Missing cells47
Missing cells (%)11.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory106.8 B

Variable types

Text4
Numeric7
Categorical1

Alerts

영업상태명 has constant value ""Constant
소재지우편번호 is highly overall correlated with WGS84위도High correlation
WGS84위도 is highly overall correlated with 소재지우편번호High correlation
자격소유인원수(명) has 8 (22.9%) missing valuesMissing
총인원수(명) has 6 (17.1%) missing valuesMissing
소재지도로명주소 has 17 (48.6%) missing valuesMissing
소재지우편번호 has 16 (45.7%) missing valuesMissing
사업장명 has unique valuesUnique
입소정원(명) has 24 (68.6%) zerosZeros
자격소유인원수(명) has 6 (17.1%) zerosZeros

Reproduction

Analysis started2023-12-10 22:18:48.641338
Analysis finished2023-12-10 22:18:53.387579
Duration4.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

Distinct27
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-11T07:18:53.488197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1142857
Min length3

Characters and Unicode

Total characters109
Distinct characters35
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

Unique20 ?
Unique (%)57.1%

Sample

1st row가평군
2nd row고양시
3rd row과천시
4th row광명시
5th row광주시
ValueCountFrequency (%)
용인시 3
 
8.6%
평택시 2
 
5.7%
의정부시 2
 
5.7%
성남시 2
 
5.7%
시흥시 2
 
5.7%
안산시 2
 
5.7%
안양시 2
 
5.7%
고양시 1
 
2.9%
하남시 1
 
2.9%
파주시 1
 
2.9%
Other values (17) 17
48.6%
2023-12-11T07:18:53.744612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 35
32.1%
안 5
 
4.6%
양 5
 
4.6%
남 4
 
3.7%
평 4
 
3.7%
주 4
 
3.7%
천 4
 
3.7%
성 4
 
3.7%
용 3
 
2.8%
군 3
 
2.8%
Other values (25) 38
34.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 109
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 35
32.1%
안 5
 
4.6%
양 5
 
4.6%
남 4
 
3.7%
평 4
 
3.7%
주 4
 
3.7%
천 4
 
3.7%
성 4
 
3.7%
용 3
 
2.8%
군 3
 
2.8%
Other values (25) 38
34.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 109
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 35
32.1%
안 5
 
4.6%
양 5
 
4.6%
남 4
 
3.7%
평 4
 
3.7%
주 4
 
3.7%
천 4
 
3.7%
성 4
 
3.7%
용 3
 
2.8%
군 3
 
2.8%
Other values (25) 38
34.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 109
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 35
32.1%
안 5
 
4.6%
양 5
 
4.6%
남 4
 
3.7%
평 4
 
3.7%
주 4
 
3.7%
천 4
 
3.7%
성 4
 
3.7%
용 3
 
2.8%
군 3
 
2.8%
Other values (25) 38
34.9%

사업장명
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-11T07:18:53.918837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length11.171429
Min length6

Characters and Unicode

Total characters391
Distinct characters73
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

Unique35 ?
Unique (%)100.0%

Sample

1st row가평군 장애인복지관
2nd row고양시장애인종합복지관
3rd row과천시장애인복지관
4th row광명장애인종합복지관
5th row성분도복지관
ValueCountFrequency (%)
장애인복지관 3
 
7.0%
시흥장애인종합복지관 2
 
4.7%
안양시관악장애인종합복지관 1
 
2.3%
경기도시각장애인복지관 1
 
2.3%
양평군 1
 
2.3%
여주시장애인복지관 1
 
2.3%
오산장애인종합복지관 1
 
2.3%
용인시수지장애인복지관 1
 
2.3%
용인시기흥장애인복지관 1
 
2.3%
용인시처인장애인복지관 1
 
2.3%
Other values (30) 30
69.8%
2023-12-11T07:18:54.214150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 38
 
9.7%
복 38
 
9.7%
지 38
 
9.7%
관 36
 
9.2%
장 34
 
8.7%
애 34
 
8.7%
시 26
 
6.6%
합 17
 
4.3%
종 16
 
4.1%
8
 
2.0%
Other values (63) 106
27.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 381
97.4%
Space Separator 8
 
2.0%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 38
 
10.0%
복 38
 
10.0%
지 38
 
10.0%
관 36
 
9.4%
장 34
 
8.9%
애 34
 
8.9%
시 26
 
6.8%
합 17
 
4.5%
종 16
 
4.2%
안 5
 
1.3%
Other values (60) 99
26.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 381
97.4%
Common 10
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 38
 
10.0%
복 38
 
10.0%
지 38
 
10.0%
관 36
 
9.4%
장 34
 
8.9%
애 34
 
8.9%
시 26
 
6.8%
합 17
 
4.5%
종 16
 
4.2%
안 5
 
1.3%
Other values (60) 99
26.0%
Common
ValueCountFrequency (%)
8
80.0%
( 1
 
10.0%
) 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 381
97.4%
ASCII 10
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 38
 
10.0%
복 38
 
10.0%
지 38
 
10.0%
관 36
 
9.4%
장 34
 
8.9%
애 34
 
8.9%
시 26
 
6.8%
합 17
 
4.5%
종 16
 
4.2%
안 5
 
1.3%
Other values (60) 99
26.0%
ASCII
ValueCountFrequency (%)
8
80.0%
( 1
 
10.0%
) 1
 
10.0%

인허가일자
Real number (ℝ)

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20062338
Minimum19911023
Maximum20171024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T07:18:54.335246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19911023
5-th percentile19947432
Q120005814
median20060622
Q320115720
95-th percentile20163699
Maximum20171024
Range260001
Interquartile range (IQR)109907

Descriptive statistics

Standard deviation70248.706
Coefficient of variation (CV)0.0035015214
Kurtosis-0.79834726
Mean20062338
Median Absolute Deviation (MAD)59591
Skewness-0.35302959
Sum7.0218183 × 108
Variance4.9348807 × 109
MonotonicityNot monotonic
2023-12-11T07:18:54.444879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
20110601 2
 
5.7%
20111228 1
 
2.9%
20130227 1
 
2.9%
20140328 1
 
2.9%
20100503 1
 
2.9%
20160601 1
 
2.9%
20120213 1
 
2.9%
20051014 1
 
2.9%
20050617 1
 
2.9%
20040722 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
19911023 1
2.9%
19941206 1
2.9%
19950101 1
2.9%
19950526 1
2.9%
19980422 1
2.9%
19990421 1
2.9%
19991130 1
2.9%
20000811 1
2.9%
20001019 1
2.9%
20010608 1
2.9%
ValueCountFrequency (%)
20171024 1
2.9%
20170927 1
2.9%
20160601 1
2.9%
20140328 1
2.9%
20131028 1
2.9%
20130227 1
2.9%
20130111 1
2.9%
20130102 1
2.9%
20120213 1
2.9%
20111228 1
2.9%

영업상태명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
운영중
35 

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 35
100.0%

Length

2023-12-11T07:18:54.567040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:18:54.661659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 35
100.0%

입소정원(명)
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.57143
Minimum0
Maximum1000
Zeros24
Zeros (%)68.6%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T07:18:54.733720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3100
95-th percentile580
Maximum1000
Range1000
Interquartile range (IQR)100

Descriptive statistics

Standard deviation250.34027
Coefficient of variation (CV)2.1475268
Kurtosis7.7767461
Mean116.57143
Median Absolute Deviation (MAD)0
Skewness2.7834325
Sum4080
Variance62670.252
MonotonicityNot monotonic
2023-12-11T07:18:54.824907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 24
68.6%
200 2
 
5.7%
100 2
 
5.7%
1000 2
 
5.7%
400 2
 
5.7%
300 2
 
5.7%
80 1
 
2.9%
ValueCountFrequency (%)
0 24
68.6%
80 1
 
2.9%
100 2
 
5.7%
200 2
 
5.7%
300 2
 
5.7%
400 2
 
5.7%
1000 2
 
5.7%
ValueCountFrequency (%)
1000 2
 
5.7%
400 2
 
5.7%
300 2
 
5.7%
200 2
 
5.7%
100 2
 
5.7%
80 1
 
2.9%
0 24
68.6%

자격소유인원수(명)
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)59.3%
Missing8
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean15.444444
Minimum0
Maximum36
Zeros6
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T07:18:54.908461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.5
median14
Q323.5
95-th percentile32.1
Maximum36
Range36
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.342003
Coefficient of variation (CV)0.73437428
Kurtosis-1.0925704
Mean15.444444
Median Absolute Deviation (MAD)11
Skewness0.017994888
Sum417
Variance128.64103
MonotonicityNot monotonic
2023-12-11T07:18:55.002750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 6
17.1%
14 2
 
5.7%
30 2
 
5.7%
21 2
 
5.7%
20 2
 
5.7%
13 2
 
5.7%
10 2
 
5.7%
28 1
 
2.9%
26 1
 
2.9%
36 1
 
2.9%
Other values (6) 6
17.1%
(Missing) 8
22.9%
ValueCountFrequency (%)
0 6
17.1%
3 1
 
2.9%
10 2
 
5.7%
11 1
 
2.9%
13 2
 
5.7%
14 2
 
5.7%
17 1
 
2.9%
20 2
 
5.7%
21 2
 
5.7%
22 1
 
2.9%
ValueCountFrequency (%)
36 1
2.9%
33 1
2.9%
30 2
5.7%
28 1
2.9%
26 1
2.9%
25 1
2.9%
22 1
2.9%
21 2
5.7%
20 2
5.7%
17 1
2.9%

총인원수(명)
Real number (ℝ)

MISSING 

Distinct25
Distinct (%)86.2%
Missing6
Missing (%)17.1%
Infinite0
Infinite (%)0.0%
Mean29.413793
Minimum1
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T07:18:55.099918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.4
Q123
median30
Q337
95-th percentile47
Maximum63
Range62
Interquartile range (IQR)14

Descriptive statistics

Standard deviation14.50743
Coefficient of variation (CV)0.4932186
Kurtosis0.24270745
Mean29.413793
Median Absolute Deviation (MAD)7
Skewness-0.2035705
Sum853
Variance210.46552
MonotonicityNot monotonic
2023-12-11T07:18:55.407629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
30 2
 
5.7%
26 2
 
5.7%
47 2
 
5.7%
36 2
 
5.7%
63 1
 
2.9%
9 1
 
2.9%
13 1
 
2.9%
23 1
 
2.9%
20 1
 
2.9%
24 1
 
2.9%
Other values (15) 15
42.9%
(Missing) 6
 
17.1%
ValueCountFrequency (%)
1 1
2.9%
2 1
2.9%
3 1
2.9%
9 1
2.9%
13 1
2.9%
20 1
2.9%
21 1
2.9%
23 1
2.9%
24 1
2.9%
26 2
5.7%
ValueCountFrequency (%)
63 1
2.9%
47 2
5.7%
46 1
2.9%
45 1
2.9%
41 1
2.9%
38 1
2.9%
37 1
2.9%
36 2
5.7%
35 1
2.9%
34 1
2.9%

소재지도로명주소
Text

MISSING 

Distinct17
Distinct (%)94.4%
Missing17
Missing (%)48.6%
Memory size412.0 B
2023-12-11T07:18:55.576914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length18.388889
Min length14

Characters and Unicode

Total characters331
Distinct characters76
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

Unique16 ?
Unique (%)88.9%

Sample

1st row경기도 고양시 일산서구 탄현로 139
2nd row경기도 과천시 문원로 40
3rd row경기도 광명시 목감로 120
4th row경기도 구리시 이문안로 86-1
5th row경기도 군포시 청백리길 18
ValueCountFrequency (%)
경기도 18
 
22.8%
시흥시 2
 
2.5%
만안구 2
 
2.5%
용인시 2
 
2.5%
안양시 2
 
2.5%
27-1 2
 
2.5%
정왕대로233번길 2
 
2.5%
역곡로 1
 
1.3%
367 1
 
1.3%
동두천시 1
 
1.3%
Other values (46) 46
58.2%
2023-12-11T07:18:55.852994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61
18.4%
경 20
 
6.0%
시 20
 
6.0%
기 19
 
5.7%
도 18
 
5.4%
로 17
 
5.1%
3 16
 
4.8%
1 11
 
3.3%
2 9
 
2.7%
구 8
 
2.4%
Other values (66) 132
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 205
61.9%
Decimal Number 62
 
18.7%
Space Separator 61
 
18.4%
Dash Punctuation 3
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 20
 
9.8%
시 20
 
9.8%
기 19
 
9.3%
도 18
 
8.8%
로 17
 
8.3%
구 8
 
3.9%
안 7
 
3.4%
길 6
 
2.9%
천 5
 
2.4%
원 4
 
2.0%
Other values (55) 81
39.5%
Decimal Number
ValueCountFrequency (%)
3 16
25.8%
1 11
17.7%
2 9
14.5%
6 6
 
9.7%
8 4
 
6.5%
4 4
 
6.5%
0 4
 
6.5%
9 4
 
6.5%
7 4
 
6.5%
Space Separator
ValueCountFrequency (%)
61
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 205
61.9%
Common 126
38.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 20
 
9.8%
시 20
 
9.8%
기 19
 
9.3%
도 18
 
8.8%
로 17
 
8.3%
구 8
 
3.9%
안 7
 
3.4%
길 6
 
2.9%
천 5
 
2.4%
원 4
 
2.0%
Other values (55) 81
39.5%
Common
ValueCountFrequency (%)
61
48.4%
3 16
 
12.7%
1 11
 
8.7%
2 9
 
7.1%
6 6
 
4.8%
8 4
 
3.2%
4 4
 
3.2%
0 4
 
3.2%
9 4
 
3.2%
7 4
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 205
61.9%
ASCII 126
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
61
48.4%
3 16
 
12.7%
1 11
 
8.7%
2 9
 
7.1%
6 6
 
4.8%
8 4
 
3.2%
4 4
 
3.2%
0 4
 
3.2%
9 4
 
3.2%
7 4
 
3.2%
Hangul
ValueCountFrequency (%)
경 20
 
9.8%
시 20
 
9.8%
기 19
 
9.3%
도 18
 
8.8%
로 17
 
8.3%
구 8
 
3.9%
안 7
 
3.4%
길 6
 
2.9%
천 5
 
2.4%
원 4
 
2.0%
Other values (55) 81
39.5%

소재지지번주소
Text

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-11T07:18:56.080055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length21
Mean length18.6
Min length11

Characters and Unicode

Total characters651
Distinct characters102
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

Unique33 ?
Unique (%)94.3%

Sample

1st row경기도 가평군 가평읍 읍내리
2nd row경기도 고양시 일산서구 탄현동 111-1번지
3rd row경기도 과천시 문원동 31-3번지
4th row경기도 광명시 광명동 164-2번지
5th row경기도 광주시 도척면
ValueCountFrequency (%)
경기도 35
 
24.3%
용인시 3
 
2.1%
안산시 2
 
1.4%
안양동 2
 
1.4%
시흥시 2
 
1.4%
양평군 2
 
1.4%
평택시 2
 
1.4%
의정부시 2
 
1.4%
안양시 2
 
1.4%
성남시 2
 
1.4%
Other values (87) 90
62.5%
2023-12-11T07:18:56.433086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
109
 
16.7%
시 39
 
6.0%
기 37
 
5.7%
도 36
 
5.5%
경 35
 
5.4%
동 31
 
4.8%
지 26
 
4.0%
1 21
 
3.2%
번 18
 
2.8%
- 15
 
2.3%
Other values (92) 284
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 455
69.9%
Space Separator 109
 
16.7%
Decimal Number 72
 
11.1%
Dash Punctuation 15
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 39
 
8.6%
기 37
 
8.1%
도 36
 
7.9%
경 35
 
7.7%
동 31
 
6.8%
지 26
 
5.7%
번 18
 
4.0%
구 11
 
2.4%
안 11
 
2.4%
인 10
 
2.2%
Other values (80) 201
44.2%
Decimal Number
ValueCountFrequency (%)
1 21
29.2%
0 9
12.5%
4 8
 
11.1%
8 6
 
8.3%
5 6
 
8.3%
7 6
 
8.3%
3 5
 
6.9%
2 4
 
5.6%
9 4
 
5.6%
6 3
 
4.2%
Space Separator
ValueCountFrequency (%)
109
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 455
69.9%
Common 196
30.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 39
 
8.6%
기 37
 
8.1%
도 36
 
7.9%
경 35
 
7.7%
동 31
 
6.8%
지 26
 
5.7%
번 18
 
4.0%
구 11
 
2.4%
안 11
 
2.4%
인 10
 
2.2%
Other values (80) 201
44.2%
Common
ValueCountFrequency (%)
109
55.6%
1 21
 
10.7%
- 15
 
7.7%
0 9
 
4.6%
4 8
 
4.1%
8 6
 
3.1%
5 6
 
3.1%
7 6
 
3.1%
3 5
 
2.6%
2 4
 
2.0%
Other values (2) 7
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 455
69.9%
ASCII 196
30.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
109
55.6%
1 21
 
10.7%
- 15
 
7.7%
0 9
 
4.6%
4 8
 
4.1%
8 6
 
3.1%
5 6
 
3.1%
7 6
 
3.1%
3 5
 
2.6%
2 4
 
2.0%
Other values (2) 7
 
3.6%
Hangul
ValueCountFrequency (%)
시 39
 
8.6%
기 37
 
8.1%
도 36
 
7.9%
경 35
 
7.7%
동 31
 
6.8%
지 26
 
5.7%
번 18
 
4.0%
구 11
 
2.4%
안 11
 
2.4%
인 10
 
2.2%
Other values (80) 201
44.2%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)94.7%
Missing16
Missing (%)45.7%
Infinite0
Infinite (%)0.0%
Mean14223.316
Minimum10239
Maximum18427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T07:18:56.542669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10239
5-th percentile10749.3
Q112088
median14284
Q315608
95-th percentile17679.1
Maximum18427
Range8188
Interquartile range (IQR)3520

Descriptive statistics

Standard deviation2365.305
Coefficient of variation (CV)0.16629772
Kurtosis-0.84505121
Mean14223.316
Median Absolute Deviation (MAD)2048
Skewness-0.00033678604
Sum270243
Variance5594667.6
MonotonicityNot monotonic
2023-12-11T07:18:56.633541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
15055 2
 
5.7%
17596 1
 
2.9%
18427 1
 
2.9%
10806 1
 
2.9%
11804 1
 
2.9%
17038 1
 
2.9%
16905 1
 
2.9%
14091 1
 
2.9%
13914 1
 
2.9%
10239 1
 
2.9%
Other values (8) 8
22.9%
(Missing) 16
45.7%
ValueCountFrequency (%)
10239 1
2.9%
10806 1
2.9%
11338 1
2.9%
11804 1
2.9%
11940 1
2.9%
12236 1
2.9%
13828 1
2.9%
13914 1
2.9%
14091 1
2.9%
14284 1
2.9%
ValueCountFrequency (%)
18427 1
2.9%
17596 1
2.9%
17038 1
2.9%
16905 1
2.9%
15829 1
2.9%
15387 1
2.9%
15055 2
5.7%
14471 1
2.9%
14284 1
2.9%
14091 1
2.9%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.429855
Minimum36.965789
Maximum37.907102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T07:18:56.731776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.965789
5-th percentile37.04733
Q137.306888
median37.384748
Q337.581376
95-th percentile37.850292
Maximum37.907102
Range0.94131215
Interquartile range (IQR)0.27448747

Descriptive statistics

Standard deviation0.23210484
Coefficient of variation (CV)0.0062010616
Kurtosis-0.13671307
Mean37.429855
Median Absolute Deviation (MAD)0.11990619
Skewness0.25054332
Sum1310.0449
Variance0.053872656
MonotonicityNot monotonic
2023-12-11T07:18:56.836401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
37.3475321894 2
 
5.7%
37.8357777189 1
 
2.9%
37.38474824 1
 
2.9%
37.3019364498 1
 
2.9%
37.1781765187 1
 
2.9%
37.3240316619 1
 
2.9%
37.3152053856 1
 
2.9%
37.259324508 1
 
2.9%
37.3481642506 1
 
2.9%
37.7478075662 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
36.9657894585 1
2.9%
36.9947196201 1
2.9%
37.0698766766 1
2.9%
37.1781765187 1
2.9%
37.2051071381 1
2.9%
37.259324508 1
2.9%
37.2648420458 1
2.9%
37.2986632641 1
2.9%
37.3019364498 1
2.9%
37.3118402609 1
2.9%
ValueCountFrequency (%)
37.907101612 1
2.9%
37.8841575639 1
2.9%
37.8357777189 1
2.9%
37.7478075662 1
2.9%
37.7436003276 1
2.9%
37.7031209843 1
2.9%
37.6413930999 1
2.9%
37.6268389866 1
2.9%
37.5914519737 1
2.9%
37.5712996713 1
2.9%

WGS84경도
Real number (ℝ)

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.0639
Minimum126.68025
Maximum127.63545
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T07:18:56.946353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.68025
5-th percentile126.74108
Q1126.89807
median127.0541
Q3127.18008
95-th percentile127.50393
Maximum127.63545
Range0.95519935
Interquartile range (IQR)0.28200047

Descriptive statistics

Standard deviation0.2317772
Coefficient of variation (CV)0.0018240995
Kurtosis0.083441745
Mean127.0639
Median Absolute Deviation (MAD)0.14270749
Skewness0.56823089
Sum4447.2365
Variance0.05372067
MonotonicityNot monotonic
2023-12-11T07:18:57.080234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
126.7410833109 2
 
5.7%
127.5108932167 1
 
2.9%
126.9272291195 1
 
2.9%
127.6354543405 1
 
2.9%
127.047811991 1
 
2.9%
127.0876464761 1
 
2.9%
127.1056862559 1
 
2.9%
127.2189235473 1
 
2.9%
127.0039879921 1
 
2.9%
127.0642546015 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
126.6802549862 1
2.9%
126.7410833109 2
5.7%
126.7648528403 1
2.9%
126.8064624309 1
2.9%
126.813653058 1
2.9%
126.8308158801 1
2.9%
126.846819565 1
2.9%
126.8754432654 1
2.9%
126.920706294 1
2.9%
126.9272291195 1
2.9%
ValueCountFrequency (%)
127.6354543405 1
2.9%
127.5108932167 1
2.9%
127.5009410395 1
2.9%
127.4325269226 1
2.9%
127.3209481287 1
2.9%
127.284776369 1
2.9%
127.2189235473 1
2.9%
127.2003626557 1
2.9%
127.1968087466 1
2.9%
127.1633417439 1
2.9%

Interactions

2023-12-11T07:18:52.525964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:49.081800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:49.559939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:50.444583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:51.018808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:51.510060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:52.050893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:52.638005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:49.149716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:49.642435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:50.538162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:51.093097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:51.582703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:52.122555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:52.716976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:49.214143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:49.718968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:50.624303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:51.162093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:51.658911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:52.187208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:52.787033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:49.290319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:50.101069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:50.711157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:51.234011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:51.741502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:52.258805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:52.855057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:49.362522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:50.186011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:50.789726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:51.308328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:51.831064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:52.329793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:52.920240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:49.428008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:50.282972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:50.866787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:51.372674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:51.908143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:52.394353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:52.989759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:49.493758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:50.360720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:50.943784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:51.442730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:51.983290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:18:52.460346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:18:57.156629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업장명인허가일자입소정원(명)자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
시군명1.0001.0000.8100.8470.4660.0001.0001.0001.0000.9680.976
사업장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
인허가일자0.8101.0001.0000.0000.4370.0000.9310.9430.5580.0000.588
입소정원(명)0.8471.0000.0001.0000.6950.0001.0001.0000.0000.0000.605
자격소유인원수(명)0.4661.0000.4370.6951.0000.6690.9570.9730.0000.6740.534
총인원수(명)0.0001.0000.0000.0000.6691.0001.0001.0000.7390.0000.668
소재지도로명주소1.0001.0000.9311.0000.9571.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0000.9431.0000.9731.0001.0001.0001.0001.0001.000
소재지우편번호1.0001.0000.5580.0000.0000.7391.0001.0001.0000.7720.296
WGS84위도0.9681.0000.0000.0000.6740.0001.0001.0000.7721.0000.527
WGS84경도0.9761.0000.5880.6050.5340.6681.0001.0000.2960.5271.000
2023-12-11T07:18:57.266220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자입소정원(명)자격소유인원수(명)총인원수(명)소재지우편번호WGS84위도WGS84경도
인허가일자1.0000.2530.050-0.3990.222-0.1370.291
입소정원(명)0.2531.0000.1980.0150.0630.0160.180
자격소유인원수(명)0.0500.1981.0000.373-0.1470.102-0.369
총인원수(명)-0.3990.0150.3731.0000.1090.050-0.308
소재지우편번호0.2220.063-0.1470.1091.000-0.9460.187
WGS84위도-0.1370.0160.1020.050-0.9461.000-0.136
WGS84경도0.2910.180-0.369-0.3080.187-0.1361.000

Missing values

2023-12-11T07:18:53.094057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:18:53.233896image/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.
2023-12-11T07:18:53.334025image/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 장애인복지관20111228운영중0103<NA>경기도 가평군 가평읍 읍내리<NA>37.835778127.510893
1고양시고양시장애인종합복지관20040402운영중02136경기도 고양시 일산서구 탄현로 139경기도 고양시 일산서구 탄현동 111-1번지1023937.703121126.764853
2과천시과천시장애인복지관20110601운영중2001446경기도 과천시 문원로 40경기도 과천시 문원동 31-3번지1382837.428778127.001978
3광명시광명장애인종합복지관20001019운영중803037경기도 광명시 목감로 120경기도 광명시 광명동 164-2번지1428437.478734126.84682
4광주시성분도복지관20010608운영중0<NA><NA><NA>경기도 광주시 도척면<NA>37.31184127.320948
5구리시구리시장애인종합복지관20000811운영중02833경기도 구리시 이문안로 86-1경기도 구리시 수택동 851-1번지1194037.591452127.141007
6군포시군포시장애인종합복지관19991130운영중02634경기도 군포시 청백리길 18경기도 군포시 금정동 844-1번지1582937.363002126.934125
7김포시김포시 장애인복지관20130102운영중03638<NA>경기도 김포시 장기동<NA>37.641393126.680255
8남양주시남양주시장애인복지관20060622운영중03336경기도 남양주시 홍유릉로 273경기도 남양주시 금곡동 615-10번지1223637.626839127.200363
9동두천시동두천시장애인종합복지관20060907운영중0026경기도 동두천시 상패로 64경기도 동두천시 상패동 54번지1133837.907102127.047922
시군명사업장명인허가일자영업상태명입소정원(명)자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
25용인시용인시처인장애인복지관20050617운영중0041경기도 용인시 처인구 경안천로 318경기도 용인시 처인구 고림동 954-1번지1703837.259325127.218924
26의왕시희망나래장애인복지관20110601운영중02126<NA>경기도 의왕시 고천동 희망나래장애인복지관<NA>37.348164127.003988
27의정부시경기도시각장애인복지관20130227운영중02024<NA>경기도 의정부시 신곡동<NA>37.747808127.064255
28의정부시의정부시장애인종합복지관20040722운영중3001730경기도 의정부시 용민로 160경기도 의정부시 민락동 735-17번지1180437.7436127.092116
29이천시이천시장애인종합복지관20090622운영중0020<NA>경기도 이천시 중리동 이천시장애인종합복지관<NA>37.264842127.432527
30파주시파주시장애인종합복지관19980422운영중0023경기도 파주시 법원읍 술이홀로1333번길 63경기도 파주시 법원읍 금곡리 428-3번지1080637.884158126.875443
31평택시에바다장애인종합복지관19950101운영중02230<NA>경기도 평택시 팽성읍 남산리<NA>36.965789127.064553
32평택시평택북부장애인복지관20130111운영중01313<NA>경기도 평택시 서정동<NA>37.069877127.054101
33하남시하남시장애인복지관20170927운영중1000<NA><NA><NA>경기도 하남시 풍산동<NA>37.5713127.196809
34화성시나래울 장애인복지관(나래울 복합복지타운)20110111운영중0139경기도 화성시 여울로2길 33경기도 화성시 능동 1130번지1842737.205107127.051557