Overview

Dataset statistics

Number of variables7
Number of observations56
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory64.4 B

Variable types

Text1
Numeric6

Dataset

Description한국사회복지협의회 사회공헌활동 기부은행 돌봄봉사자 현황데이터로 지역별 인원수, 활동횟수, 활동시간에 대한 통계를 제공합니다.
URLhttps://www.data.go.kr/data/15066440/fileData.do

Alerts

인원수(남) is highly overall correlated with 인원수(여) and 2 other fieldsHigh correlation
인원수(여) is highly overall correlated with 인원수(남) and 2 other fieldsHigh correlation
활동횟수(남) is highly overall correlated with 인원수(남) and 3 other fieldsHigh correlation
활동횟수(여) is highly overall correlated with 활동횟수(남) and 1 other fieldsHigh correlation
활동시간(남) is highly overall correlated with 인원수(남) and 3 other fieldsHigh correlation
활동시간(여) is highly overall correlated with 인원수(여) and 3 other fieldsHigh correlation
지역(시군구) has unique valuesUnique
활동횟수(여) has unique valuesUnique
활동시간(여) has unique valuesUnique
인원수(남) has 3 (5.4%) zerosZeros
활동횟수(남) has 3 (5.4%) zerosZeros
활동시간(남) has 3 (5.4%) zerosZeros

Reproduction

Analysis started2023-12-12 20:04:36.800420
Analysis finished2023-12-12 20:04:41.404836
Duration4.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역(시군구)
Text

UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size580.0 B
2023-12-13T05:04:41.613478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.9464286
Min length5

Characters and Unicode

Total characters333
Distinct characters69
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

Unique56 ?
Unique (%)100.0%

Sample

1st row서울 중랑구
2nd row서울 서대문구
3rd row서울 영등포구
4th row서울 서초구
5th row부산 동구
ValueCountFrequency (%)
경기 11
 
9.8%
동구 5
 
4.5%
서울 4
 
3.6%
충북 4
 
3.6%
충남 4
 
3.6%
부산 4
 
3.6%
경남 4
 
3.6%
대구 3
 
2.7%
전북 3
 
2.7%
경북 3
 
2.7%
Other values (59) 67
59.8%
2023-12-13T05:04:42.148056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
56
 
16.8%
시 30
 
9.0%
구 21
 
6.3%
경 19
 
5.7%
남 14
 
4.2%
주 12
 
3.6%
기 11
 
3.3%
북 10
 
3.0%
군 9
 
2.7%
전 9
 
2.7%
Other values (59) 142
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 277
83.2%
Space Separator 56
 
16.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 30
 
10.8%
구 21
 
7.6%
경 19
 
6.9%
남 14
 
5.1%
주 12
 
4.3%
기 11
 
4.0%
북 10
 
3.6%
군 9
 
3.2%
전 9
 
3.2%
서 9
 
3.2%
Other values (58) 133
48.0%
Space Separator
ValueCountFrequency (%)
56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 277
83.2%
Common 56
 
16.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 30
 
10.8%
구 21
 
7.6%
경 19
 
6.9%
남 14
 
5.1%
주 12
 
4.3%
기 11
 
4.0%
북 10
 
3.6%
군 9
 
3.2%
전 9
 
3.2%
서 9
 
3.2%
Other values (58) 133
48.0%
Common
ValueCountFrequency (%)
56
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 277
83.2%
ASCII 56
 
16.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
56
100.0%
Hangul
ValueCountFrequency (%)
시 30
 
10.8%
구 21
 
7.6%
경 19
 
6.9%
남 14
 
5.1%
주 12
 
4.3%
기 11
 
4.0%
북 10
 
3.6%
군 9
 
3.2%
전 9
 
3.2%
서 9
 
3.2%
Other values (58) 133
48.0%

인원수(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.5
Minimum0
Maximum731
Zeros3
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-13T05:04:42.339338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q122.5
median52
Q387
95-th percentile298
Maximum731
Range731
Interquartile range (IQR)64.5

Descriptive statistics

Standard deviation130.10318
Coefficient of variation (CV)1.4700924
Kurtosis12.615342
Mean88.5
Median Absolute Deviation (MAD)34
Skewness3.327377
Sum4956
Variance16926.836
MonotonicityNot monotonic
2023-12-13T05:04:42.520723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 3
 
5.4%
75 2
 
3.6%
8 2
 
3.6%
39 2
 
3.6%
4 2
 
3.6%
52 2
 
3.6%
12 2
 
3.6%
74 1
 
1.8%
99 1
 
1.8%
27 1
 
1.8%
Other values (38) 38
67.9%
ValueCountFrequency (%)
0 3
5.4%
4 2
3.6%
6 1
 
1.8%
8 2
3.6%
11 1
 
1.8%
12 2
3.6%
16 1
 
1.8%
17 1
 
1.8%
18 1
 
1.8%
24 1
 
1.8%
ValueCountFrequency (%)
731 1
1.8%
534 1
1.8%
412 1
1.8%
260 1
1.8%
236 1
1.8%
201 1
1.8%
171 1
1.8%
155 1
1.8%
137 1
1.8%
135 1
1.8%

인원수(여)
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.89286
Minimum7
Maximum1183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-13T05:04:42.704519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile30
Q1111.75
median190
Q3305.75
95-th percentile702.25
Maximum1183
Range1176
Interquartile range (IQR)194

Descriptive statistics

Standard deviation221.03251
Coefficient of variation (CV)0.90256822
Kurtosis6.1674758
Mean244.89286
Median Absolute Deviation (MAD)98.5
Skewness2.2177924
Sum13714
Variance48855.37
MonotonicityNot monotonic
2023-12-13T05:04:42.928169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 2
 
3.6%
115 2
 
3.6%
116 2
 
3.6%
187 2
 
3.6%
212 1
 
1.8%
131 1
 
1.8%
414 1
 
1.8%
168 1
 
1.8%
253 1
 
1.8%
92 1
 
1.8%
Other values (42) 42
75.0%
ValueCountFrequency (%)
7 1
1.8%
28 1
1.8%
30 2
3.6%
38 1
1.8%
49 1
1.8%
51 1
1.8%
71 1
1.8%
79 1
1.8%
81 1
1.8%
91 1
1.8%
ValueCountFrequency (%)
1183 1
1.8%
880 1
1.8%
805 1
1.8%
668 1
1.8%
501 1
1.8%
463 1
1.8%
462 1
1.8%
414 1
1.8%
409 1
1.8%
351 1
1.8%

활동횟수(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1491.2857
Minimum0
Maximum7429
Zeros3
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-13T05:04:43.096125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.5
Q1502.25
median1012
Q31986.5
95-th percentile4808.5
Maximum7429
Range7429
Interquartile range (IQR)1484.25

Descriptive statistics

Standard deviation1553.2605
Coefficient of variation (CV)1.041558
Kurtosis4.1353789
Mean1491.2857
Median Absolute Deviation (MAD)717
Skewness1.8996292
Sum83512
Variance2412618.3
MonotonicityNot monotonic
2023-12-13T05:04:43.253597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
5.4%
798 2
 
3.6%
4081 1
 
1.8%
125 1
 
1.8%
2455 1
 
1.8%
784 1
 
1.8%
102 1
 
1.8%
564 1
 
1.8%
3547 1
 
1.8%
1722 1
 
1.8%
Other values (43) 43
76.8%
ValueCountFrequency (%)
0 3
5.4%
30 1
 
1.8%
43 1
 
1.8%
71 1
 
1.8%
77 1
 
1.8%
102 1
 
1.8%
125 1
 
1.8%
278 1
 
1.8%
306 1
 
1.8%
310 1
 
1.8%
ValueCountFrequency (%)
7429 1
1.8%
6181 1
1.8%
4834 1
1.8%
4800 1
1.8%
4081 1
1.8%
3547 1
1.8%
2957 1
1.8%
2549 1
1.8%
2455 1
1.8%
2334 1
1.8%

활동횟수(여)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8885.7143
Minimum61
Maximum92265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-13T05:04:43.432577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile335
Q12703.75
median5478
Q39103.5
95-th percentile24280.5
Maximum92265
Range92204
Interquartile range (IQR)6399.75

Descriptive statistics

Standard deviation13136.65
Coefficient of variation (CV)1.4784012
Kurtosis29.985857
Mean8885.7143
Median Absolute Deviation (MAD)3223.5
Skewness4.9185086
Sum497600
Variance1.7257158 × 108
MonotonicityNot monotonic
2023-12-13T05:04:43.626916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
685 1
 
1.8%
9058 1
 
1.8%
5527 1
 
1.8%
5083 1
 
1.8%
13506 1
 
1.8%
4101 1
 
1.8%
382 1
 
1.8%
8797 1
 
1.8%
16634 1
 
1.8%
7576 1
 
1.8%
Other values (46) 46
82.1%
ValueCountFrequency (%)
61 1
1.8%
170 1
1.8%
194 1
1.8%
382 1
1.8%
685 1
1.8%
782 1
1.8%
1348 1
1.8%
1456 1
1.8%
1473 1
1.8%
2037 1
1.8%
ValueCountFrequency (%)
92265 1
1.8%
27486 1
1.8%
25149 1
1.8%
23991 1
1.8%
22986 1
1.8%
18876 1
1.8%
17710 1
1.8%
16634 1
1.8%
16391 1
1.8%
13506 1
1.8%

활동시간(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3324.7143
Minimum0
Maximum23408.25
Zeros3
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-13T05:04:43.794574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile63
Q1801.625
median1960.25
Q34498.3125
95-th percentile10167.375
Maximum23408.25
Range23408.25
Interquartile range (IQR)3696.6875

Descriptive statistics

Standard deviation4028.4834
Coefficient of variation (CV)1.2116781
Kurtosis10.501117
Mean3324.7143
Median Absolute Deviation (MAD)1526.125
Skewness2.7194746
Sum186184
Variance16228678
MonotonicityNot monotonic
2023-12-13T05:04:43.969266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
5.4%
2962.75 1
 
1.8%
7575.25 1
 
1.8%
262.0 1
 
1.8%
7459.25 1
 
1.8%
1164.0 1
 
1.8%
163.0 1
 
1.8%
1441.0 1
 
1.8%
10386.75 1
 
1.8%
2295.0 1
 
1.8%
Other values (44) 44
78.6%
ValueCountFrequency (%)
0.0 3
5.4%
84.0 1
 
1.8%
101.0 1
 
1.8%
139.0 1
 
1.8%
163.0 1
 
1.8%
194.5 1
 
1.8%
262.0 1
 
1.8%
473.75 1
 
1.8%
516.0 1
 
1.8%
525.5 1
 
1.8%
ValueCountFrequency (%)
23408.25 1
1.8%
12514.25 1
1.8%
10386.75 1
1.8%
10094.25 1
1.8%
7714.5 1
1.8%
7575.25 1
1.8%
7459.25 1
1.8%
7062.0 1
1.8%
7061.25 1
1.8%
6973.5 1
1.8%

활동시간(여)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15677.438
Minimum169
Maximum92766.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-13T05:04:44.145943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum169
5-th percentile613.375
Q15234.125
median11839.625
Q318213.375
95-th percentile44741.188
Maximum92766.5
Range92597.5
Interquartile range (IQR)12979.25

Descriptive statistics

Standard deviation16966.305
Coefficient of variation (CV)1.0822116
Kurtosis8.4255102
Mean15677.438
Median Absolute Deviation (MAD)6449.25
Skewness2.5720083
Sum877936.5
Variance2.8785552 × 108
MonotonicityNot monotonic
2023-12-13T05:04:44.334633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500.0 1
 
1.8%
15363.75 1
 
1.8%
12126.0 1
 
1.8%
8077.25 1
 
1.8%
33013.25 1
 
1.8%
5837.75 1
 
1.8%
656.5 1
 
1.8%
20555.5 1
 
1.8%
43756.5 1
 
1.8%
12277.25 1
 
1.8%
Other values (46) 46
82.1%
ValueCountFrequency (%)
169.0 1
1.8%
276.0 1
1.8%
484.0 1
1.8%
656.5 1
1.8%
1407.0 1
1.8%
1500.0 1
1.8%
2347.0 1
1.8%
2428.0 1
1.8%
2918.75 1
1.8%
3042.25 1
1.8%
ValueCountFrequency (%)
92766.5 1
1.8%
70866.5 1
1.8%
47695.25 1
1.8%
43756.5 1
1.8%
40684.75 1
1.8%
33013.25 1
1.8%
29987.5 1
1.8%
27178.75 1
1.8%
26564.5 1
1.8%
23293.5 1
1.8%

Interactions

2023-12-13T05:04:40.170631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:37.135609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:37.754546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:38.360615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:38.981593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:39.569404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:40.288165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:37.244797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:37.863278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:38.467005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:39.089365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:39.677069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:40.407791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:37.340853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:37.962307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:38.555865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:39.180122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:39.774063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:40.523551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:37.443572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:38.073317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:38.669851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:39.287972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:39.905455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:40.630067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:37.545835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:38.168767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:38.772117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:39.385216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:39.992957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:40.729623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:37.654203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:38.266368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:38.877633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:39.487066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:40.079354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:04:44.434955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역(시군구)인원수(남)인원수(여)활동횟수(남)활동횟수(여)활동시간(남)활동시간(여)
지역(시군구)1.0001.0001.0001.0001.0001.0001.000
인원수(남)1.0001.0000.8100.6260.0000.8140.000
인원수(여)1.0000.8101.0000.0000.4080.1430.606
활동횟수(남)1.0000.6260.0001.0000.8070.8700.815
활동횟수(여)1.0000.0000.4080.8071.0000.4630.976
활동시간(남)1.0000.8140.1430.8700.4631.0000.700
활동시간(여)1.0000.0000.6060.8150.9760.7001.000
2023-12-13T05:04:44.613377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인원수(남)인원수(여)활동횟수(남)활동횟수(여)활동시간(남)활동시간(여)
인원수(남)1.0000.7280.5040.1000.5550.213
인원수(여)0.7281.0000.4770.4530.5110.592
활동횟수(남)0.5040.4771.0000.5450.9580.626
활동횟수(여)0.1000.4530.5451.0000.4680.906
활동시간(남)0.5550.5110.9580.4681.0000.628
활동시간(여)0.2130.5920.6260.9060.6281.000

Missing values

2023-12-13T05:04:41.207358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:04:41.342494image/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

지역(시군구)인원수(남)인원수(여)활동횟수(남)활동횟수(여)활동시간(남)활동시간(여)
0서울 중랑구03006850.01500.0
1서울 서대문구731805233429446973.58769.0
2서울 영등포구1711548341639110094.2523293.5
3서울 서초구201351254954297062.011121.5
4부산 동구7566878763382424.521572.0
5부산 동래구471933111473754.752918.75
6부산 남구13533483338841443.756685.75
7부산 해운대구7122481976981576.2511377.25
8대구 동구23629879825121714.254194.75
9대구 수성구4830858973191144.015477.75
지역(시군구)인원수(남)인원수(여)활동횟수(남)활동횟수(여)활동시간(남)활동시간(여)
46전남 장흥군181781656188762280.022576.5
47경북 김천시827530623991516.026564.5
48경북 경산시64116133121191973.53473.5
49경북 칠곡군86496225263525.52347.0
50경남 진주시123843170101.0276.0
51경남 김해시3611371782139.01407.0
52경남 거제시4145774693194.511311.25
53경남 거창군4123136181478423408.2518155.0
54제주 제주시171501213592403156.014194.25
55제주 서귀포시524632789702870.2527178.75