Overview

Dataset statistics

Number of variables13
Number of observations45
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory116.9 B

Variable types

Categorical2
Text1
Numeric10

Dataset

Description인천광역시 사랑의 그린PC 보급현황(대분류(개인,단체) 중분류(장애인,저소득층 등) 등에 따른 pc보급수량) 정보
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15048963&srcSe=7661IVAWM27C61E190

Alerts

2015_단체,개인수 is highly overall correlated with 2015_수량 and 8 other fieldsHigh correlation
2015_수량 is highly overall correlated with 2015_단체,개인수 and 8 other fieldsHigh correlation
2016_단체,개인수 is highly overall correlated with 2015_단체,개인수 and 8 other fieldsHigh correlation
2016_수량 is highly overall correlated with 2015_단체,개인수 and 8 other fieldsHigh correlation
2017_단체,개인수 is highly overall correlated with 2015_단체,개인수 and 8 other fieldsHigh correlation
2017_수량 is highly overall correlated with 2015_단체,개인수 and 8 other fieldsHigh correlation
2018_단체,개인수 is highly overall correlated with 2015_단체,개인수 and 8 other fieldsHigh correlation
2018_수량 is highly overall correlated with 2015_단체,개인수 and 8 other fieldsHigh correlation
2019_단체,개인수 is highly overall correlated with 2015_단체,개인수 and 8 other fieldsHigh correlation
2019_수량 is highly overall correlated with 2015_단체,개인수 and 8 other fieldsHigh correlation
대분류 is highly overall correlated with 중분류High correlation
중분류 is highly overall correlated with 대분류High correlation
2015_단체,개인수 has 31 (68.9%) zerosZeros
2015_수량 has 31 (68.9%) zerosZeros
2016_단체,개인수 has 27 (60.0%) zerosZeros
2016_수량 has 27 (60.0%) zerosZeros
2017_단체,개인수 has 26 (57.8%) zerosZeros
2017_수량 has 26 (57.8%) zerosZeros
2018_단체,개인수 has 29 (64.4%) zerosZeros
2018_수량 has 29 (64.4%) zerosZeros
2019_단체,개인수 has 28 (62.2%) zerosZeros
2019_수량 has 28 (62.2%) zerosZeros

Reproduction

Analysis started2024-01-28 15:56:10.584265
Analysis finished2024-01-28 15:56:18.074899
Duration7.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size492.0 B
단체
29 
개인
16 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
단체 29
64.4%
개인 16
35.6%

Length

2024-01-29T00:56:18.122429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T00:56:18.199723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단체 29
64.4%
개인 16
35.6%

중분류
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Memory size492.0 B
해외
5 
종합사회복지시설
5 
기타시설
4 
노인시설
4 
다문화가정지원센터
4 
Other values (14)
23 

Length

Max length16
Median length9
Mean length5.5777778
Min length2

Unique

Unique9 ?
Unique (%)20.0%

Sample

1st row장애인
2nd row장애인
3rd row장애인
4th row장애인
5th row저소득층

Common Values

ValueCountFrequency (%)
해외 5
11.1%
종합사회복지시설 5
11.1%
기타시설 4
8.9%
노인시설 4
8.9%
다문화가정지원센터 4
8.9%
장애인 4
8.9%
저소득층 3
 
6.7%
아동시설 3
 
6.7%
북한이탈주민 2
 
4.4%
차상위계층 2
 
4.4%
Other values (9) 9
20.0%

Length

2024-01-29T00:56:18.296614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
해외 5
10.6%
종합사회복지시설 5
10.6%
기타시설 4
 
8.5%
노인시설 4
 
8.5%
다문화가정지원센터 4
 
8.5%
장애인 4
 
8.5%
저소득층 3
 
6.4%
아동시설 3
 
6.4%
북한이탈주민 2
 
4.3%
차상위계층 2
 
4.3%
Other values (11) 11
23.4%

소분류
Text

Distinct44
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size492.0 B
2024-01-29T00:56:18.477263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length6.1555556
Min length2

Characters and Unicode

Total characters277
Distinct characters98
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)95.6%

Sample

1st row시각장애인
2nd row청각장애인
3rd row지체장애인
4th row기타장애인
5th row기초생활수급자
ValueCountFrequency (%)
기타 4
 
7.4%
기타취약계층 2
 
3.7%
농어촌지역 1
 
1.9%
기타노인시설 1
 
1.9%
국가유공자 1
 
1.9%
단체 1
 
1.9%
이주여성시설 1
 
1.9%
결혼이민자시설 1
 
1.9%
외국인노동자시설 1
 
1.9%
다문화시설 1
 
1.9%
Other values (40) 40
74.1%
2024-01-29T00:56:18.758090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 14
 
5.1%
기 12
 
4.3%
설 12
 
4.3%
인 12
 
4.3%
타 10
 
3.6%
9
 
3.2%
장 9
 
3.2%
애 8
 
2.9%
이 7
 
2.5%
민 6
 
2.2%
Other values (88) 178
64.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 256
92.4%
Space Separator 9
 
3.2%
Open Punctuation 3
 
1.1%
Close Punctuation 3
 
1.1%
Uppercase Letter 3
 
1.1%
Decimal Number 2
 
0.7%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 14
 
5.5%
기 12
 
4.7%
설 12
 
4.7%
인 12
 
4.7%
타 10
 
3.9%
장 9
 
3.5%
애 8
 
3.1%
이 7
 
2.7%
민 6
 
2.3%
자 6
 
2.3%
Other values (79) 160
62.5%
Uppercase Letter
ValueCountFrequency (%)
O 1
33.3%
N 1
33.3%
G 1
33.3%
Decimal Number
ValueCountFrequency (%)
6 1
50.0%
5 1
50.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 256
92.4%
Common 18
 
6.5%
Latin 3
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 14
 
5.5%
기 12
 
4.7%
설 12
 
4.7%
인 12
 
4.7%
타 10
 
3.9%
장 9
 
3.5%
애 8
 
3.1%
이 7
 
2.7%
민 6
 
2.3%
자 6
 
2.3%
Other values (79) 160
62.5%
Common
ValueCountFrequency (%)
9
50.0%
( 3
 
16.7%
) 3
 
16.7%
, 1
 
5.6%
6 1
 
5.6%
5 1
 
5.6%
Latin
ValueCountFrequency (%)
O 1
33.3%
N 1
33.3%
G 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 256
92.4%
ASCII 21
 
7.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 14
 
5.5%
기 12
 
4.7%
설 12
 
4.7%
인 12
 
4.7%
타 10
 
3.9%
장 9
 
3.5%
애 8
 
3.1%
이 7
 
2.7%
민 6
 
2.3%
자 6
 
2.3%
Other values (79) 160
62.5%
ASCII
ValueCountFrequency (%)
9
42.9%
( 3
 
14.3%
) 3
 
14.3%
O 1
 
4.8%
N 1
 
4.8%
, 1
 
4.8%
G 1
 
4.8%
6 1
 
4.8%
5 1
 
4.8%

2015_단체,개인수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.333333
Minimum0
Maximum262
Zeros31
Zeros (%)68.9%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-01-29T00:56:18.877414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile33.4
Maximum262
Range262
Interquartile range (IQR)4

Descriptive statistics

Standard deviation39.622078
Coefficient of variation (CV)3.8343947
Kurtosis39.200352
Mean10.333333
Median Absolute Deviation (MAD)0
Skewness6.1003905
Sum465
Variance1569.9091
MonotonicityNot monotonic
2024-01-29T00:56:18.965313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 31
68.9%
5 2
 
4.4%
21 1
 
2.2%
6 1
 
2.2%
27 1
 
2.2%
35 1
 
2.2%
262 1
 
2.2%
25 1
 
2.2%
4 1
 
2.2%
12 1
 
2.2%
Other values (4) 4
 
8.9%
ValueCountFrequency (%)
0 31
68.9%
1 1
 
2.2%
3 1
 
2.2%
4 1
 
2.2%
5 2
 
4.4%
6 1
 
2.2%
12 1
 
2.2%
19 1
 
2.2%
21 1
 
2.2%
25 1
 
2.2%
ValueCountFrequency (%)
262 1
2.2%
40 1
2.2%
35 1
2.2%
27 1
2.2%
25 1
2.2%
21 1
2.2%
19 1
2.2%
12 1
2.2%
6 1
2.2%
5 2
4.4%

2015_수량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.977778
Minimum0
Maximum262
Zeros31
Zeros (%)68.9%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-01-29T00:56:19.047098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile33.4
Maximum262
Range262
Interquartile range (IQR)6

Descriptive statistics

Standard deviation39.620638
Coefficient of variation (CV)3.6091674
Kurtosis38.773623
Mean10.977778
Median Absolute Deviation (MAD)0
Skewness6.0509904
Sum494
Variance1569.7949
MonotonicityNot monotonic
2024-01-29T00:56:19.129360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 31
68.9%
6 2
 
4.4%
12 2
 
4.4%
21 1
 
2.2%
27 1
 
2.2%
35 1
 
2.2%
262 1
 
2.2%
25 1
 
2.2%
4 1
 
2.2%
40 1
 
2.2%
Other values (3) 3
 
6.7%
ValueCountFrequency (%)
0 31
68.9%
3 1
 
2.2%
4 1
 
2.2%
6 2
 
4.4%
12 2
 
4.4%
19 1
 
2.2%
21 1
 
2.2%
22 1
 
2.2%
25 1
 
2.2%
27 1
 
2.2%
ValueCountFrequency (%)
262 1
2.2%
40 1
2.2%
35 1
2.2%
27 1
2.2%
25 1
2.2%
22 1
2.2%
21 1
2.2%
19 1
2.2%
12 2
4.4%
6 2
4.4%

2016_단체,개인수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3333333
Minimum0
Maximum231
Zeros27
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-01-29T00:56:19.208704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile31
Maximum231
Range231
Interquartile range (IQR)2

Descriptive statistics

Standard deviation34.944762
Coefficient of variation (CV)3.7440816
Kurtosis38.981654
Mean9.3333333
Median Absolute Deviation (MAD)0
Skewness6.0785034
Sum420
Variance1221.1364
MonotonicityNot monotonic
2024-01-29T00:56:19.282073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 27
60.0%
1 4
 
8.9%
2 3
 
6.7%
14 2
 
4.4%
7 1
 
2.2%
33 1
 
2.2%
23 1
 
2.2%
231 1
 
2.2%
18 1
 
2.2%
8 1
 
2.2%
Other values (3) 3
 
6.7%
ValueCountFrequency (%)
0 27
60.0%
1 4
 
8.9%
2 3
 
6.7%
4 1
 
2.2%
7 1
 
2.2%
8 1
 
2.2%
14 2
 
4.4%
18 1
 
2.2%
21 1
 
2.2%
23 1
 
2.2%
ValueCountFrequency (%)
231 1
2.2%
37 1
2.2%
33 1
2.2%
23 1
2.2%
21 1
2.2%
18 1
2.2%
14 2
4.4%
8 1
2.2%
7 1
2.2%
4 1
2.2%

2016_수량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4444444
Minimum0
Maximum231
Zeros27
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-01-29T00:56:19.369096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile31
Maximum231
Range231
Interquartile range (IQR)2

Descriptive statistics

Standard deviation34.950976
Coefficient of variation (CV)3.7006916
Kurtosis38.867146
Mean9.4444444
Median Absolute Deviation (MAD)0
Skewness6.0655393
Sum425
Variance1221.5707
MonotonicityNot monotonic
2024-01-29T00:56:19.457462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 27
60.0%
1 4
 
8.9%
2 3
 
6.7%
14 1
 
2.2%
7 1
 
2.2%
33 1
 
2.2%
23 1
 
2.2%
231 1
 
2.2%
18 1
 
2.2%
8 1
 
2.2%
Other values (4) 4
 
8.9%
ValueCountFrequency (%)
0 27
60.0%
1 4
 
8.9%
2 3
 
6.7%
6 1
 
2.2%
7 1
 
2.2%
8 1
 
2.2%
14 1
 
2.2%
17 1
 
2.2%
18 1
 
2.2%
21 1
 
2.2%
ValueCountFrequency (%)
231 1
2.2%
37 1
2.2%
33 1
2.2%
23 1
2.2%
21 1
2.2%
18 1
2.2%
17 1
2.2%
14 1
2.2%
8 1
2.2%
7 1
2.2%

2017_단체,개인수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6444444
Minimum0
Maximum197
Zeros26
Zeros (%)57.8%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-01-29T00:56:19.535481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile26.8
Maximum197
Range197
Interquartile range (IQR)5

Descriptive statistics

Standard deviation29.687123
Coefficient of variation (CV)3.8834899
Kurtosis39.923737
Mean7.6444444
Median Absolute Deviation (MAD)0
Skewness6.183413
Sum344
Variance881.32525
MonotonicityNot monotonic
2024-01-29T00:56:19.619915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 26
57.8%
1 5
 
11.1%
9 2
 
4.4%
7 2
 
4.4%
8 2
 
4.4%
30 1
 
2.2%
14 1
 
2.2%
197 1
 
2.2%
6 1
 
2.2%
32 1
 
2.2%
Other values (3) 3
 
6.7%
ValueCountFrequency (%)
0 26
57.8%
1 5
 
11.1%
3 1
 
2.2%
4 1
 
2.2%
5 1
 
2.2%
6 1
 
2.2%
7 2
 
4.4%
8 2
 
4.4%
9 2
 
4.4%
14 1
 
2.2%
ValueCountFrequency (%)
197 1
2.2%
32 1
2.2%
30 1
2.2%
14 1
2.2%
9 2
4.4%
8 2
4.4%
7 2
4.4%
6 1
2.2%
5 1
2.2%
4 1
2.2%

2017_수량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2222222
Minimum0
Maximum197
Zeros26
Zeros (%)57.8%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-01-29T00:56:19.706288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile28
Maximum197
Range197
Interquartile range (IQR)7

Descriptive statistics

Standard deviation29.702991
Coefficient of variation (CV)3.6125259
Kurtosis39.308579
Mean8.2222222
Median Absolute Deviation (MAD)0
Skewness6.1147217
Sum370
Variance882.26768
MonotonicityNot monotonic
2024-01-29T00:56:19.790340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 26
57.8%
7 3
 
6.7%
1 3
 
6.7%
9 2
 
4.4%
8 2
 
4.4%
4 2
 
4.4%
30 1
 
2.2%
14 1
 
2.2%
197 1
 
2.2%
6 1
 
2.2%
Other values (3) 3
 
6.7%
ValueCountFrequency (%)
0 26
57.8%
1 3
 
6.7%
4 2
 
4.4%
5 1
 
2.2%
6 1
 
2.2%
7 3
 
6.7%
8 2
 
4.4%
9 2
 
4.4%
14 1
 
2.2%
20 1
 
2.2%
ValueCountFrequency (%)
197 1
 
2.2%
32 1
 
2.2%
30 1
 
2.2%
20 1
 
2.2%
14 1
 
2.2%
9 2
4.4%
8 2
4.4%
7 3
6.7%
6 1
 
2.2%
5 1
 
2.2%

2018_단체,개인수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2222222
Minimum0
Maximum300
Zeros29
Zeros (%)64.4%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-01-29T00:56:19.870919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile8.8
Maximum300
Range300
Interquartile range (IQR)2

Descriptive statistics

Standard deviation44.607831
Coefficient of variation (CV)5.4252767
Kurtosis44.468158
Mean8.2222222
Median Absolute Deviation (MAD)0
Skewness6.6512211
Sum370
Variance1989.8586
MonotonicityNot monotonic
2024-01-29T00:56:20.170324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 29
64.4%
1 4
 
8.9%
2 3
 
6.7%
6 2
 
4.4%
7 1
 
2.2%
300 1
 
2.2%
9 1
 
2.2%
3 1
 
2.2%
8 1
 
2.2%
17 1
 
2.2%
ValueCountFrequency (%)
0 29
64.4%
1 4
 
8.9%
2 3
 
6.7%
3 1
 
2.2%
4 1
 
2.2%
6 2
 
4.4%
7 1
 
2.2%
8 1
 
2.2%
9 1
 
2.2%
17 1
 
2.2%
ValueCountFrequency (%)
300 1
 
2.2%
17 1
 
2.2%
9 1
 
2.2%
8 1
 
2.2%
7 1
 
2.2%
6 2
4.4%
4 1
 
2.2%
3 1
 
2.2%
2 3
6.7%
1 4
8.9%

2018_수량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4444444
Minimum0
Maximum300
Zeros29
Zeros (%)64.4%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-01-29T00:56:20.245900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile11.4
Maximum300
Range300
Interquartile range (IQR)2

Descriptive statistics

Standard deviation44.601037
Coefficient of variation (CV)5.2817018
Kurtosis44.35213
Mean8.4444444
Median Absolute Deviation (MAD)0
Skewness6.6386972
Sum380
Variance1989.2525
MonotonicityNot monotonic
2024-01-29T00:56:20.323356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 29
64.4%
1 4
 
8.9%
2 2
 
4.4%
6 2
 
4.4%
7 1
 
2.2%
300 1
 
2.2%
9 1
 
2.2%
4 1
 
2.2%
12 1
 
2.2%
3 1
 
2.2%
Other values (2) 2
 
4.4%
ValueCountFrequency (%)
0 29
64.4%
1 4
 
8.9%
2 2
 
4.4%
3 1
 
2.2%
4 1
 
2.2%
6 2
 
4.4%
7 1
 
2.2%
8 1
 
2.2%
9 1
 
2.2%
12 1
 
2.2%
ValueCountFrequency (%)
300 1
2.2%
17 1
2.2%
12 1
2.2%
9 1
2.2%
8 1
2.2%
7 1
2.2%
6 2
4.4%
4 1
2.2%
3 1
2.2%
2 2
4.4%

2019_단체,개인수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3111111
Minimum0
Maximum190
Zeros28
Zeros (%)62.2%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-01-29T00:56:20.399250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile7.8
Maximum190
Range190
Interquartile range (IQR)1

Descriptive statistics

Standard deviation28.242314
Coefficient of variation (CV)5.3175904
Kurtosis44.424006
Mean5.3111111
Median Absolute Deviation (MAD)0
Skewness6.6463347
Sum239
Variance797.62828
MonotonicityNot monotonic
2024-01-29T00:56:20.474921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 28
62.2%
1 6
 
13.3%
2 5
 
11.1%
7 1
 
2.2%
3 1
 
2.2%
6 1
 
2.2%
190 1
 
2.2%
8 1
 
2.2%
9 1
 
2.2%
ValueCountFrequency (%)
0 28
62.2%
1 6
 
13.3%
2 5
 
11.1%
3 1
 
2.2%
6 1
 
2.2%
7 1
 
2.2%
8 1
 
2.2%
9 1
 
2.2%
190 1
 
2.2%
ValueCountFrequency (%)
190 1
 
2.2%
9 1
 
2.2%
8 1
 
2.2%
7 1
 
2.2%
6 1
 
2.2%
3 1
 
2.2%
2 5
 
11.1%
1 6
 
13.3%
0 28
62.2%

2019_수량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5555556
Minimum0
Maximum190
Zeros28
Zeros (%)62.2%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-01-29T00:56:20.553106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile7.8
Maximum190
Range190
Interquartile range (IQR)2

Descriptive statistics

Standard deviation28.218752
Coefficient of variation (CV)5.0793754
Kurtosis44.330915
Mean5.5555556
Median Absolute Deviation (MAD)0
Skewness6.6360666
Sum250
Variance796.29798
MonotonicityNot monotonic
2024-01-29T00:56:20.628777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 28
62.2%
2 4
 
8.9%
3 3
 
6.7%
1 3
 
6.7%
5 2
 
4.4%
7 1
 
2.2%
6 1
 
2.2%
190 1
 
2.2%
8 1
 
2.2%
9 1
 
2.2%
ValueCountFrequency (%)
0 28
62.2%
1 3
 
6.7%
2 4
 
8.9%
3 3
 
6.7%
5 2
 
4.4%
6 1
 
2.2%
7 1
 
2.2%
8 1
 
2.2%
9 1
 
2.2%
190 1
 
2.2%
ValueCountFrequency (%)
190 1
 
2.2%
9 1
 
2.2%
8 1
 
2.2%
7 1
 
2.2%
6 1
 
2.2%
5 2
 
4.4%
3 3
 
6.7%
2 4
 
8.9%
1 3
 
6.7%
0 28
62.2%

Interactions

2024-01-29T00:56:17.182275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:10.964599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:11.803974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.425952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:13.048072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:13.673723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:14.351730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:15.007010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:15.899988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.527727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:17.243860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:11.023558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:11.862859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.482403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:13.111975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:13.736177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:14.413459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:15.067938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:15.960024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.590025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:17.308394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:11.310576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:11.920410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.541314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:13.175061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:13.800014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:14.473974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:15.126704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.018739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.654746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:17.367186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:11.364674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:11.978997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.594264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:13.239350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:13.859076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:14.538310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:15.186228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.075364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.712767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:17.426156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:11.420064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.035805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.649622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:13.292042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:13.930236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:14.598548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:15.245618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.132778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.773115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:17.508184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:11.486585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.103667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.713613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:13.356172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:14.008895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:14.670090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:15.553422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.201114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.841498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:17.594515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:11.552823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.169877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.784236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:13.421240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:14.079995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:14.739040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:15.628594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.271252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.911039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:17.660686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:11.613905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.238890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.857801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:13.479747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:14.146102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:14.806836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:15.702271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.333607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.974109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:17.727332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:11.677057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.298222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.919466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:13.549112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:14.210003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:14.870651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:15.768907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.395503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:17.040348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:17.792560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:11.741833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.363533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:12.982431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:13.610327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:14.281997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:14.939360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:15.836098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:16.461036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:56:17.115218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T00:56:20.702765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류중분류소분류2015_단체,개인수2015_수량2016_단체,개인수2016_수량2017_단체,개인수2017_수량2018_단체,개인수2018_수량2019_단체,개인수2019_수량
대분류1.0001.0001.0000.2280.2280.1810.1810.1810.1070.0000.0000.0000.000
중분류1.0001.0000.9680.5660.5660.5170.5170.5170.0000.0000.0000.0000.000
소분류1.0000.9681.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2015_단체,개인수0.2280.5661.0001.0001.0000.9940.9940.9940.9811.0001.0001.0001.000
2015_수량0.2280.5661.0001.0001.0000.9940.9940.9940.9811.0001.0001.0001.000
2016_단체,개인수0.1810.5171.0000.9940.9941.0001.0001.0000.9941.0001.0001.0001.000
2016_수량0.1810.5171.0000.9940.9941.0001.0001.0000.9941.0001.0001.0001.000
2017_단체,개인수0.1810.5171.0000.9940.9941.0001.0001.0000.9941.0001.0001.0001.000
2017_수량0.1070.0001.0000.9810.9810.9940.9940.9941.0001.0001.0001.0001.000
2018_단체,개인수0.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.6740.6740.674
2018_수량0.0000.0001.0001.0001.0001.0001.0001.0001.0000.6741.0000.6740.674
2019_단체,개인수0.0000.0001.0001.0001.0001.0001.0001.0001.0000.6740.6741.0000.674
2019_수량0.0000.0001.0001.0001.0001.0001.0001.0001.0000.6740.6740.6741.000
2024-01-29T00:56:20.813197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류중분류
대분류1.0000.778
중분류0.7781.000
2024-01-29T00:56:20.890458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2015_단체,개인수2015_수량2016_단체,개인수2016_수량2017_단체,개인수2017_수량2018_단체,개인수2018_수량2019_단체,개인수2019_수량대분류중분류
2015_단체,개인수1.0000.9970.7120.7110.6500.6400.6950.6870.6910.6780.3670.272
2015_수량0.9971.0000.6950.6950.6330.6260.6850.6780.6670.6580.3670.272
2016_단체,개인수0.7120.6951.0001.0000.8860.8850.7990.7960.8200.7960.2930.236
2016_수량0.7110.6951.0001.0000.8850.8850.7990.7970.8190.7950.2930.236
2017_단체,개인수0.6500.6330.8860.8851.0000.9950.7890.7850.8540.8170.2930.236
2017_수량0.6400.6260.8850.8850.9951.0000.7860.7860.8450.8190.1720.000
2018_단체,개인수0.6950.6850.7990.7990.7890.7861.0000.9990.7720.7740.0000.000
2018_수량0.6870.6780.7960.7970.7850.7860.9991.0000.7630.7690.0000.000
2019_단체,개인수0.6910.6670.8200.8190.8540.8450.7720.7631.0000.9830.0000.000
2019_수량0.6780.6580.7960.7950.8170.8190.7740.7690.9831.0000.0000.000
대분류0.3670.3670.2930.2930.2930.1720.0000.0000.0000.0001.0000.778
중분류0.2720.2720.2360.2360.2360.0000.0000.0000.0000.0000.7781.000

Missing values

2024-01-29T00:56:17.885877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T00:56:18.010596image/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

대분류중분류소분류2015_단체,개인수2015_수량2016_단체,개인수2016_수량2017_단체,개인수2017_수량2018_단체,개인수2018_수량2019_단체,개인수2019_수량
0개인장애인시각장애인21211414992277
1개인장애인청각장애인6677771133
2개인장애인지체장애인2727333330307766
3개인장애인기타장애인3535232314146622
4개인저소득층기초생활수급자262262231231197197300300190190
5개인저소득층차상위계층25251818779988
6개인저소득층기타취약계층0000000011
7개인소년소녀가장소년소녀가장0000110000
8개인국가유공자(상이등급판정자)국가유공자(상이등급판정자)4488881122
9개인다문화가정다문화가정12122121661122
대분류중분류소분류2015_단체,개인수2015_수량2016_단체,개인수2016_수량2017_단체,개인수2017_수량2018_단체,개인수2018_수량2019_단체,개인수2019_수량
35단체농어촌지역 시설(마을회관 등)농어촌지역 시설(마을회관 등)0000000000
36단체기타시설사회시민단체0022140013
37단체기타시설대안학교0000140000
38단체기타시설기타단체5121417494813
39단체기타시설마을회관0000110011
40단체해외정부기관0000000000
41단체해외NGO0000000000
42단체해외재외공관0000000000
43단체해외학교시설0000000000
44단체해외기타0000000000