Overview

Dataset statistics

Number of variables11
Number of observations44
Missing cells134
Missing cells (%)27.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory99.0 B

Variable types

Text3
Numeric6
Categorical2

Dataset

Description대전시 노인일자리 및 사회활동 지원사업 현황으로 노인일자리 수행기관, 사업유형별 사업단, 사업량 등의 자료를 제공합니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15063354/fileData.do

Alerts

취업알선형(사업량) is highly overall correlated with 공익활동(사업단) and 5 other fieldsHigh correlation
취업알선형(사업단) is highly overall correlated with 공익활동(사업단) and 6 other fieldsHigh correlation
공익활동(사업단) is highly overall correlated with 전담 and 2 other fieldsHigh correlation
사회서비스(사업단) is highly overall correlated with 사회서비스(사업량) and 4 other fieldsHigh correlation
시장형(사업단) is highly overall correlated with 사회서비스(사업량) and 3 other fieldsHigh correlation
사회서비스(사업량) is highly overall correlated with 사회서비스(사업단) and 5 other fieldsHigh correlation
시장형(사업량) is highly overall correlated with 사회서비스(사업단) and 5 other fieldsHigh correlation
전담 is highly overall correlated with 공익활동(사업단) and 6 other fieldsHigh correlation
취업알선형(사업단) is highly imbalanced (56.1%)Imbalance
취업알선형(사업량) is highly imbalanced (71.2%)Imbalance
기관명 has 1 (2.3%) missing valuesMissing
소재지 has 2 (4.5%) missing valuesMissing
공익활동(사업단) has 4 (9.1%) missing valuesMissing
사회서비스(사업단) has 30 (68.2%) missing valuesMissing
시장형(사업단) has 28 (63.6%) missing valuesMissing
공익활동(사업량) has 4 (9.1%) missing valuesMissing
사회서비스(사업량) has 30 (68.2%) missing valuesMissing
시장형(사업량) has 28 (63.6%) missing valuesMissing
전담 has 7 (15.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 08:51:32.526534
Analysis finished2023-12-12 08:51:38.353092
Duration5.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관명
Text

MISSING 

Distinct43
Distinct (%)100.0%
Missing1
Missing (%)2.3%
Memory size484.0 B
2023-12-12T17:51:38.539758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.7209302
Min length3

Characters and Unicode

Total characters289
Distinct characters80
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

Unique43 ?
Unique (%)100.0%

Sample

1st row대동복지관
2nd row동구시니어클럽
3rd row동구청
4th row동구평생학습
5th row노인회 동구지회
ValueCountFrequency (%)
노인회 6
 
12.2%
대동복지관 1
 
2.0%
호동복지재단 1
 
2.0%
유성구노인복지관 1
 
2.0%
서구지회 1
 
2.0%
둔산사회복지관 1
 
2.0%
월평사회복지관 1
 
2.0%
유등노인복지관 1
 
2.0%
정림사회복지관 1
 
2.0%
유성구지회 1
 
2.0%
Other values (34) 34
69.4%
2023-12-12T17:51:38.941598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 24
 
8.3%
회 22
 
7.6%
복 20
 
6.9%
구 19
 
6.6%
관 18
 
6.2%
인 12
 
4.2%
노 11
 
3.8%
사 9
 
3.1%
유 8
 
2.8%
시 7
 
2.4%
Other values (70) 139
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 283
97.9%
Space Separator 6
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 24
 
8.5%
회 22
 
7.8%
복 20
 
7.1%
구 19
 
6.7%
관 18
 
6.4%
인 12
 
4.2%
노 11
 
3.9%
사 9
 
3.2%
유 8
 
2.8%
시 7
 
2.5%
Other values (69) 133
47.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 283
97.9%
Common 6
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 24
 
8.5%
회 22
 
7.8%
복 20
 
7.1%
구 19
 
6.7%
관 18
 
6.4%
인 12
 
4.2%
노 11
 
3.9%
사 9
 
3.2%
유 8
 
2.8%
시 7
 
2.5%
Other values (69) 133
47.0%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 283
97.9%
ASCII 6
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 24
 
8.5%
회 22
 
7.8%
복 20
 
7.1%
구 19
 
6.7%
관 18
 
6.4%
인 12
 
4.2%
노 11
 
3.9%
사 9
 
3.2%
유 8
 
2.8%
시 7
 
2.5%
Other values (69) 133
47.0%
ASCII
ValueCountFrequency (%)
6
100.0%

소재지
Text

MISSING 

Distinct41
Distinct (%)97.6%
Missing2
Missing (%)4.5%
Memory size484.0 B
2023-12-12T17:51:39.287366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length26
Mean length22.666667
Min length15

Characters and Unicode

Total characters952
Distinct characters86
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)95.2%

Sample

1st row대전광역시 동구 백룡로 48번길79(대동)
2nd row대전광역시 동구 동대전로 328
3rd row대전광역시 동구 동구청로 147
4th row대전광역시 동구 동구청로 147
5th row대전광역시 동구 대전로 815번길 55 (정동)
ValueCountFrequency (%)
대전광역시 41
22.3%
중구 11
 
6.0%
서구 10
 
5.4%
동구 8
 
4.3%
대덕구 7
 
3.8%
유성구 6
 
3.3%
100 2
 
1.1%
대전로1032번길 2
 
1.1%
3층 2
 
1.1%
대흥동 2
 
1.1%
Other values (89) 93
50.5%
2023-12-12T17:51:39.912993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
156
 
16.4%
대 63
 
6.6%
전 48
 
5.0%
구 46
 
4.8%
광 42
 
4.4%
시 42
 
4.4%
로 41
 
4.3%
역 41
 
4.3%
1 40
 
4.2%
동 38
 
4.0%
Other values (76) 395
41.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 567
59.6%
Decimal Number 173
 
18.2%
Space Separator 156
 
16.4%
Close Punctuation 24
 
2.5%
Open Punctuation 24
 
2.5%
Dash Punctuation 6
 
0.6%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 63
 
11.1%
전 48
 
8.5%
구 46
 
8.1%
광 42
 
7.4%
시 42
 
7.4%
로 41
 
7.2%
역 41
 
7.2%
동 38
 
6.7%
번 18
 
3.2%
길 18
 
3.2%
Other values (61) 170
30.0%
Decimal Number
ValueCountFrequency (%)
1 40
23.1%
2 25
14.5%
3 20
11.6%
6 17
9.8%
4 16
 
9.2%
5 14
 
8.1%
0 14
 
8.1%
8 12
 
6.9%
7 9
 
5.2%
9 6
 
3.5%
Space Separator
ValueCountFrequency (%)
156
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 567
59.6%
Common 385
40.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 63
 
11.1%
전 48
 
8.5%
구 46
 
8.1%
광 42
 
7.4%
시 42
 
7.4%
로 41
 
7.2%
역 41
 
7.2%
동 38
 
6.7%
번 18
 
3.2%
길 18
 
3.2%
Other values (61) 170
30.0%
Common
ValueCountFrequency (%)
156
40.5%
1 40
 
10.4%
2 25
 
6.5%
) 24
 
6.2%
( 24
 
6.2%
3 20
 
5.2%
6 17
 
4.4%
4 16
 
4.2%
5 14
 
3.6%
0 14
 
3.6%
Other values (5) 35
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 567
59.6%
ASCII 385
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
156
40.5%
1 40
 
10.4%
2 25
 
6.5%
) 24
 
6.2%
( 24
 
6.2%
3 20
 
5.2%
6 17
 
4.4%
4 16
 
4.2%
5 14
 
3.6%
0 14
 
3.6%
Other values (5) 35
 
9.1%
Hangul
ValueCountFrequency (%)
대 63
 
11.1%
전 48
 
8.5%
구 46
 
8.1%
광 42
 
7.4%
시 42
 
7.4%
로 41
 
7.2%
역 41
 
7.2%
동 38
 
6.7%
번 18
 
3.2%
길 18
 
3.2%
Other values (61) 170
30.0%

공익활동(사업단)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)27.5%
Missing4
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean4.3
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T17:51:40.076765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.75
median4
Q36
95-th percentile9.05
Maximum11
Range10
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation2.8483013
Coefficient of variation (CV)0.66239566
Kurtosis-0.55933247
Mean4.3
Median Absolute Deviation (MAD)2
Skewness0.53514461
Sum172
Variance8.1128205
MonotonicityNot monotonic
2023-12-12T17:51:40.231856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 10
22.7%
5 6
13.6%
4 6
13.6%
2 4
 
9.1%
7 3
 
6.8%
6 3
 
6.8%
9 2
 
4.5%
3 2
 
4.5%
8 2
 
4.5%
10 1
 
2.3%
(Missing) 4
 
9.1%
ValueCountFrequency (%)
1 10
22.7%
2 4
 
9.1%
3 2
 
4.5%
4 6
13.6%
5 6
13.6%
6 3
 
6.8%
7 3
 
6.8%
8 2
 
4.5%
9 2
 
4.5%
10 1
 
2.3%
ValueCountFrequency (%)
11 1
 
2.3%
10 1
 
2.3%
9 2
 
4.5%
8 2
 
4.5%
7 3
6.8%
6 3
6.8%
5 6
13.6%
4 6
13.6%
3 2
 
4.5%
2 4
9.1%

사회서비스(사업단)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)50.0%
Missing30
Missing (%)68.2%
Infinite0
Infinite (%)0.0%
Mean3.0714286
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T17:51:40.393177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2.5
Q33.75
95-th percentile7.7
Maximum9
Range8
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation2.4640269
Coefficient of variation (CV)0.80224132
Kurtosis1.4139231
Mean3.0714286
Median Absolute Deviation (MAD)1.5
Skewness1.3802223
Sum43
Variance6.0714286
MonotonicityNot monotonic
2023-12-12T17:51:40.565227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 5
 
11.4%
3 3
 
6.8%
2 2
 
4.5%
4 1
 
2.3%
9 1
 
2.3%
7 1
 
2.3%
5 1
 
2.3%
(Missing) 30
68.2%
ValueCountFrequency (%)
1 5
11.4%
2 2
 
4.5%
3 3
6.8%
4 1
 
2.3%
5 1
 
2.3%
7 1
 
2.3%
9 1
 
2.3%
ValueCountFrequency (%)
9 1
 
2.3%
7 1
 
2.3%
5 1
 
2.3%
4 1
 
2.3%
3 3
6.8%
2 2
 
4.5%
1 5
11.4%

시장형(사업단)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)37.5%
Missing28
Missing (%)63.6%
Infinite0
Infinite (%)0.0%
Mean3.0625
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T17:51:40.736830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q36
95-th percentile7.25
Maximum8
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.6949026
Coefficient of variation (CV)0.87996819
Kurtosis-1.1183861
Mean3.0625
Median Absolute Deviation (MAD)0.5
Skewness0.85865465
Sum49
Variance7.2625
MonotonicityNot monotonic
2023-12-12T17:51:40.876533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 8
 
18.2%
6 2
 
4.5%
7 2
 
4.5%
2 2
 
4.5%
3 1
 
2.3%
8 1
 
2.3%
(Missing) 28
63.6%
ValueCountFrequency (%)
1 8
18.2%
2 2
 
4.5%
3 1
 
2.3%
6 2
 
4.5%
7 2
 
4.5%
8 1
 
2.3%
ValueCountFrequency (%)
8 1
 
2.3%
7 2
 
4.5%
6 2
 
4.5%
3 1
 
2.3%
2 2
 
4.5%
1 8
18.2%

취업알선형(사업단)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
<NA>
40 
1
 
4

Length

Max length4
Median length4
Mean length3.7272727
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 40
90.9%
1 4
 
9.1%

Length

2023-12-12T17:51:41.045917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:51:41.195178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 40
90.9%
1 4
 
9.1%

공익활동(사업량)
Text

MISSING 

Distinct37
Distinct (%)92.5%
Missing4
Missing (%)9.1%
Memory size484.0 B
2023-12-12T17:51:41.408591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters120
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

Unique35 ?
Unique (%)87.5%

Sample

1st row722
2nd row1,550
3rd row40
4th row500
5th row550
ValueCountFrequency (%)
430 3
 
7.5%
500 2
 
5.0%
570 1
 
2.5%
708 1
 
2.5%
186 1
 
2.5%
100 1
 
2.5%
220 1
 
2.5%
298 1
 
2.5%
280 1
 
2.5%
258 1
 
2.5%
Other values (27) 27
67.5%
2023-12-12T17:51:41.859401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 29
24.2%
5 16
13.3%
2 13
10.8%
3 12
10.0%
8 12
10.0%
1 10
 
8.3%
4 8
 
6.7%
6 8
 
6.7%
7 5
 
4.2%
9 4
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 117
97.5%
Other Punctuation 3
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29
24.8%
5 16
13.7%
2 13
11.1%
3 12
10.3%
8 12
10.3%
1 10
 
8.5%
4 8
 
6.8%
6 8
 
6.8%
7 5
 
4.3%
9 4
 
3.4%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29
24.2%
5 16
13.3%
2 13
10.8%
3 12
10.0%
8 12
10.0%
1 10
 
8.3%
4 8
 
6.7%
6 8
 
6.7%
7 5
 
4.2%
9 4
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29
24.2%
5 16
13.3%
2 13
10.8%
3 12
10.0%
8 12
10.0%
1 10
 
8.3%
4 8
 
6.7%
6 8
 
6.7%
7 5
 
4.2%
9 4
 
3.3%

사회서비스(사업량)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)92.9%
Missing30
Missing (%)68.2%
Infinite0
Infinite (%)0.0%
Mean123.07143
Minimum10
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T17:51:42.029898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13.9
Q155.5
median85
Q3149.25
95-th percentile355.45
Maximum360
Range350
Interquartile range (IQR)93.75

Descriptive statistics

Standard deviation111.43226
Coefficient of variation (CV)0.90542753
Kurtosis1.213165
Mean123.07143
Median Absolute Deviation (MAD)52
Skewness1.3942287
Sum1723
Variance12417.148
MonotonicityNot monotonic
2023-12-12T17:51:42.167938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
60 2
 
4.5%
100 1
 
2.3%
144 1
 
2.3%
16 1
 
2.3%
360 1
 
2.3%
10 1
 
2.3%
353 1
 
2.3%
70 1
 
2.3%
119 1
 
2.3%
151 1
 
2.3%
Other values (3) 3
 
6.8%
(Missing) 30
68.2%
ValueCountFrequency (%)
10 1
2.3%
16 1
2.3%
40 1
2.3%
54 1
2.3%
60 2
4.5%
70 1
2.3%
100 1
2.3%
119 1
2.3%
144 1
2.3%
151 1
2.3%
ValueCountFrequency (%)
360 1
2.3%
353 1
2.3%
186 1
2.3%
151 1
2.3%
144 1
2.3%
119 1
2.3%
100 1
2.3%
70 1
2.3%
60 2
4.5%
54 1
2.3%

시장형(사업량)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)81.2%
Missing28
Missing (%)63.6%
Infinite0
Infinite (%)0.0%
Mean75.8125
Minimum5
Maximum280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T17:51:42.645982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8.75
Q118.75
median30
Q3100.25
95-th percentile235
Maximum280
Range275
Interquartile range (IQR)81.5

Descriptive statistics

Standard deviation85.305114
Coefficient of variation (CV)1.1252117
Kurtosis0.85542376
Mean75.8125
Median Absolute Deviation (MAD)22.5
Skewness1.3830606
Sum1213
Variance7276.9625
MonotonicityNot monotonic
2023-12-12T17:51:42.795607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10 2
 
4.5%
30 2
 
4.5%
25 2
 
4.5%
70 1
 
2.3%
152 1
 
2.3%
20 1
 
2.3%
280 1
 
2.3%
220 1
 
2.3%
5 1
 
2.3%
59 1
 
2.3%
Other values (3) 3
 
6.8%
(Missing) 28
63.6%
ValueCountFrequency (%)
5 1
2.3%
10 2
4.5%
15 1
2.3%
20 1
2.3%
25 2
4.5%
30 2
4.5%
59 1
2.3%
70 1
2.3%
83 1
2.3%
152 1
2.3%
ValueCountFrequency (%)
280 1
2.3%
220 1
2.3%
179 1
2.3%
152 1
2.3%
83 1
2.3%
70 1
2.3%
59 1
2.3%
30 2
4.5%
25 2
4.5%
20 1
2.3%

취업알선형(사업량)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size484.0 B
<NA>
40 
50
 
2
60
 
1
110
 
1

Length

Max length4
Median length4
Mean length3.8409091
Min length2

Unique

Unique2 ?
Unique (%)4.5%

Sample

1st row60
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 40
90.9%
50 2
 
4.5%
60 1
 
2.3%
110 1
 
2.3%

Length

2023-12-12T17:51:42.988658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:51:43.147768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 40
90.9%
50 2
 
4.5%
60 1
 
2.3%
110 1
 
2.3%

전담
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)27.0%
Missing7
Missing (%)15.9%
Infinite0
Infinite (%)0.0%
Mean3.8378378
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T17:51:43.289728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile9.2
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.004751
Coefficient of variation (CV)0.78292808
Kurtosis2.5911165
Mean3.8378378
Median Absolute Deviation (MAD)2
Skewness1.5546926
Sum142
Variance9.0285285
MonotonicityNot monotonic
2023-12-12T17:51:43.443997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 10
22.7%
1 9
20.5%
6 4
 
9.1%
4 4
 
9.1%
2 4
 
9.1%
9 2
 
4.5%
14 1
 
2.3%
5 1
 
2.3%
8 1
 
2.3%
10 1
 
2.3%
(Missing) 7
15.9%
ValueCountFrequency (%)
1 9
20.5%
2 4
 
9.1%
3 10
22.7%
4 4
 
9.1%
5 1
 
2.3%
6 4
 
9.1%
8 1
 
2.3%
9 2
 
4.5%
10 1
 
2.3%
14 1
 
2.3%
ValueCountFrequency (%)
14 1
 
2.3%
10 1
 
2.3%
9 2
 
4.5%
8 1
 
2.3%
6 4
 
9.1%
5 1
 
2.3%
4 4
 
9.1%
3 10
22.7%
2 4
 
9.1%
1 9
20.5%

Interactions

2023-12-12T17:51:36.992200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:33.125199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:34.155581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:34.938670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:35.648523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:36.337804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:37.107751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:33.235237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:34.263109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:35.068762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:35.746447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:36.432312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:37.237293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:33.366040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:34.389270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:35.194288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:35.874691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:36.534529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:37.368046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:33.489699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:34.536117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:35.329656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:35.990145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:36.660309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:37.498730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:33.963612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:34.675988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:35.442615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:36.100851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:36.771314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:37.638820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:34.053924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:34.794949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:35.547194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:36.211174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:51:36.891642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:51:43.573854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관명소재지공익활동(사업단)사회서비스(사업단)시장형(사업단)공익활동(사업량)사회서비스(사업량)시장형(사업량)취업알선형(사업량)전담
기관명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0000.9651.0001.0000.9811.0001.0001.0000.974
공익활동(사업단)1.0000.9651.0000.8600.0000.0000.0000.000NaN0.761
사회서비스(사업단)1.0001.0000.8601.0000.4511.0000.9140.9071.0000.598
시장형(사업단)1.0001.0000.0000.4511.0001.0000.5330.8820.0000.838
공익활동(사업량)1.0000.9810.0001.0001.0001.0001.0001.000NaN0.925
사회서비스(사업량)1.0001.0000.0000.9140.5331.0001.0000.8111.0000.351
시장형(사업량)1.0001.0000.0000.9070.8821.0000.8111.0001.0000.567
취업알선형(사업량)1.0001.000NaN1.0000.000NaN1.0001.0001.0001.000
전담1.0000.9740.7610.5980.8380.9250.3510.5671.0001.000
2023-12-12T17:51:43.758502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
취업알선형(사업량)취업알선형(사업단)
취업알선형(사업량)1.0001.000
취업알선형(사업단)1.0001.000
2023-12-12T17:51:43.907864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공익활동(사업단)사회서비스(사업단)시장형(사업단)사회서비스(사업량)시장형(사업량)전담취업알선형(사업단)취업알선형(사업량)
공익활동(사업단)1.0000.1350.403-0.0560.3570.7401.0001.000
사회서비스(사업단)0.1351.0000.4980.8870.7020.7661.0001.000
시장형(사업단)0.4030.4981.0000.7730.8950.7131.0000.000
사회서비스(사업량)-0.0560.8870.7731.0000.9390.6611.0001.000
시장형(사업량)0.3570.7020.8950.9391.0000.7211.0001.000
전담0.7400.7660.7130.6610.7211.0001.0001.000
취업알선형(사업단)1.0001.0001.0001.0001.0001.0001.0001.000
취업알선형(사업량)1.0001.0000.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T17:51:37.780587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:51:37.990430image/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.
2023-12-12T17:51:38.184830image/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

기관명소재지공익활동(사업단)사회서비스(사업단)시장형(사업단)취업알선형(사업단)공익활동(사업량)사회서비스(사업량)시장형(사업량)취업알선형(사업량)전담
0대동복지관대전광역시 동구 백룡로 48번길79(대동)<NA>231<NA>10070603
1동구시니어클럽대전광역시 동구 동대전로 3281038<NA>722144152<NA>9
2동구청대전광역시 동구 동구청로 1472<NA><NA><NA>1,550<NA><NA><NA>3
3동구평생학습대전광역시 동구 동구청로 1471<NA><NA><NA>40<NA><NA><NA>1
4노인회 동구지회대전광역시 동구 대전로 815번길 55 (정동)5<NA><NA><NA>500<NA><NA><NA>3
5정다운복지관대전광역시 동구 충정로 53-30 (가양동)941<NA>5506010<NA>6
6행복한복지관대전광역시 동구 동부로 23 (판암동)71<NA><NA>46016<NA><NA>4
7용운복지관대전광역시 동구 용운로 110(용운동)4<NA><NA><NA>112<NA><NA><NA>1
8중구청대전광역시 중구 중앙로 1001<NA><NA><NA>1,500<NA><NA><NA>6
9시노인복지관대전광역시 중구 테미로 26(대흥동)51<NA><NA>34060<NA><NA>3
기관명소재지공익활동(사업단)사회서비스(사업단)시장형(사업단)취업알선형(사업단)공익활동(사업량)사회서비스(사업량)시장형(사업량)취업알선형(사업량)전담
34유성시니어클럽대전광역시 유성구 유성대로752번길 36736<NA>37815183<NA>6
35유성실버복지센터대전광역시 유성구 원신흥남로27번길 54(원신흥동)3<NA>1<NA>150<NA>10<NA>1
36국제문화교류단대전광역시 대덕구 옛신탄진로 121 4층 401호512<NA>3365430<NA>3
37대덕구노인복지관대전광역시 대덕구 계족로740번길 80 (읍내동)1111<NA>9004025<NA>8
38대덕구시니어클럽대전광역시 대덕구 대전로1032번길 5185716081861795010
39대덕구청대전광역시 대덕구 대전로1033번길 201<NA><NA><NA>570<NA><NA><NA>1
40노인회 대덕구지회대전광역시 대덕구 신탄진로681번길 10 (덕암동)2<NA><NA><NA>420<NA><NA><NA>2
41참살이사회적협동조합대전광역시 대덕구 대전로1032번길 13-5 (오정동) 3층1<NA>1<NA>186<NA>15<NA>2
42사회복지법인누리봄대전광역시 대덕구 신탄진로218번길 57-9 2층2<NA>1<NA>176<NA>25<NA>2
43<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>