Overview

Dataset statistics

Number of variables5
Number of observations948
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.9 KiB
Average record size in memory44.1 B

Variable types

Categorical1
Numeric4

Dataset

Description강원도 삼척시의 각 행정구역별 단독세대 현황, 연령별 단독세대 현황, 남성 단독세대 현황, 여성 단독세대 현황을 제공합니다.
Author강원특별자치도 삼척시
URLhttps://www.data.go.kr/data/15100365/fileData.do

Alerts

계 is highly overall correlated with 남 and 1 other fieldsHigh correlation
남 is highly overall correlated with 계 and 1 other fieldsHigh correlation
여 is highly overall correlated with 계 and 1 other fieldsHigh correlation
남 has 116 (12.2%) zerosZeros
여 has 90 (9.5%) zerosZeros

Reproduction

Analysis started2024-04-06 08:32:26.872270
Analysis finished2024-04-06 08:32:31.170150
Duration4.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정기관
Categorical

Distinct13
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size7.5 KiB
도계읍
85 
성내동
84 
근덕면
81 
남양동
81 
교동
81 
Other values (8)
536 

Length

Max length8
Median length3
Mean length3.3048523
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도계읍 85
9.0%
성내동 84
8.9%
근덕면 81
8.5%
남양동 81
8.5%
교동 81
8.5%
정라동 80
8.4%
원덕읍 78
8.2%
원덕읍임원출장소 74
7.8%
미로면 69
7.3%
하장면 65
 
6.9%
Other values (3) 170
17.9%

Length

2024-04-06T17:32:31.307169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
도계읍 85
9.0%
성내동 84
8.9%
근덕면 81
8.5%
남양동 81
8.5%
교동 81
8.5%
정라동 80
8.4%
원덕읍 78
8.2%
원덕읍임원출장소 74
7.8%
미로면 69
7.3%
하장면 65
 
6.9%
Other values (3) 170
17.9%

연령
Real number (ℝ)

Distinct90
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.808017
Minimum4
Maximum103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-04-06T17:32:31.616798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile24
Q143
median61
Q380
95-th percentile95
Maximum103
Range99
Interquartile range (IQR)37

Descriptive statistics

Standard deviation22.358769
Coefficient of variation (CV)0.36769442
Kurtosis-1.034921
Mean60.808017
Median Absolute Deviation (MAD)18
Skewness-0.10613215
Sum57646
Variance499.91453
MonotonicityNot monotonic
2024-04-06T17:32:31.899428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 13
 
1.4%
73 13
 
1.4%
71 13
 
1.4%
70 13
 
1.4%
69 13
 
1.4%
68 13
 
1.4%
67 13
 
1.4%
66 13
 
1.4%
65 13
 
1.4%
64 13
 
1.4%
Other values (80) 818
86.3%
ValueCountFrequency (%)
4 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
17 2
 
0.2%
18 4
0.4%
19 4
0.4%
20 6
0.6%
21 7
0.7%
22 8
0.8%
23 9
0.9%
ValueCountFrequency (%)
103 2
 
0.2%
102 1
 
0.1%
101 5
0.5%
100 4
 
0.4%
99 5
0.5%
98 9
0.9%
97 7
0.7%
96 6
0.6%
95 10
1.1%
94 11
1.2%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.209916
Minimum1
Maximum337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-04-06T17:32:32.197745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median11
Q327
95-th percentile51
Maximum337
Range336
Interquartile range (IQR)23

Descriptive statistics

Standard deviation24.403057
Coefficient of variation (CV)1.3400972
Kurtosis60.284408
Mean18.209916
Median Absolute Deviation (MAD)9
Skewness6.0803467
Sum17263
Variance595.50922
MonotonicityNot monotonic
2024-04-06T17:32:32.501815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 94
 
9.9%
2 67
 
7.1%
6 52
 
5.5%
4 46
 
4.9%
3 41
 
4.3%
5 40
 
4.2%
7 36
 
3.8%
8 34
 
3.6%
9 33
 
3.5%
14 29
 
3.1%
Other values (63) 476
50.2%
ValueCountFrequency (%)
1 94
9.9%
2 67
7.1%
3 41
4.3%
4 46
4.9%
5 40
4.2%
6 52
5.5%
7 36
 
3.8%
8 34
 
3.6%
9 33
 
3.5%
10 17
 
1.8%
ValueCountFrequency (%)
337 1
0.1%
277 1
0.1%
249 1
0.1%
229 1
0.1%
183 1
0.1%
152 1
0.1%
139 1
0.1%
104 1
0.1%
82 1
0.1%
73 2
0.2%

남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3132911
Minimum0
Maximum149
Zeros116
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-04-06T17:32:32.795260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q313
95-th percentile29
Maximum149
Range149
Interquartile range (IQR)11

Descriptive statistics

Standard deviation12.975485
Coefficient of variation (CV)1.3932223
Kurtosis33.093816
Mean9.3132911
Median Absolute Deviation (MAD)4
Skewness4.486705
Sum8829
Variance168.3632
MonotonicityNot monotonic
2024-04-06T17:32:33.088695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 119
 
12.6%
0 116
 
12.2%
2 85
 
9.0%
4 71
 
7.5%
3 57
 
6.0%
5 45
 
4.7%
7 40
 
4.2%
6 39
 
4.1%
9 33
 
3.5%
8 31
 
3.3%
Other values (41) 312
32.9%
ValueCountFrequency (%)
0 116
12.2%
1 119
12.6%
2 85
9.0%
3 57
6.0%
4 71
7.5%
5 45
 
4.7%
6 39
 
4.1%
7 40
 
4.2%
8 31
 
3.3%
9 33
 
3.5%
ValueCountFrequency (%)
149 1
0.1%
129 1
0.1%
108 1
0.1%
98 1
0.1%
96 1
0.1%
95 1
0.1%
92 1
0.1%
85 1
0.1%
56 1
0.1%
50 1
0.1%

여
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8966245
Minimum0
Maximum188
Zeros90
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-04-06T17:32:33.370975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q313
95-th percentile27
Maximum188
Range188
Interquartile range (IQR)11

Descriptive statistics

Standard deviation13.263867
Coefficient of variation (CV)1.4908876
Kurtosis67.149475
Mean8.8966245
Median Absolute Deviation (MAD)4
Skewness6.4057835
Sum8434
Variance175.93017
MonotonicityNot monotonic
2024-04-06T17:32:33.641047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 141
14.9%
2 101
 
10.7%
0 90
 
9.5%
3 75
 
7.9%
4 66
 
7.0%
5 46
 
4.9%
6 36
 
3.8%
7 35
 
3.7%
9 28
 
3.0%
8 27
 
2.8%
Other values (40) 303
32.0%
ValueCountFrequency (%)
0 90
9.5%
1 141
14.9%
2 101
10.7%
3 75
7.9%
4 66
7.0%
5 46
 
4.9%
6 36
 
3.8%
7 35
 
3.7%
8 27
 
2.8%
9 28
 
3.0%
ValueCountFrequency (%)
188 1
0.1%
151 1
0.1%
148 1
0.1%
121 1
0.1%
91 1
0.1%
56 1
0.1%
47 2
0.2%
44 1
0.1%
43 1
0.1%
42 1
0.1%

Interactions

2024-04-06T17:32:29.765321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:32:27.267096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:32:28.069487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:32:28.945726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:32:30.048415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:32:27.434896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:32:28.264160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:32:29.156348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:32:30.318376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:32:27.665128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:32:28.481708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:32:29.353445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:32:30.543355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:32:27.878157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:32:28.715674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:32:29.558678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:32:33.840865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정기관연령계남여
행정기관1.0000.0000.3120.3810.333
연령0.0001.0000.4880.4110.352
계0.3120.4881.0000.9670.964
남0.3810.4110.9671.0000.898
여0.3330.3520.9640.8981.000
2024-04-06T17:32:34.031269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령계남여행정기관
연령1.000-0.091-0.3910.1980.000
계-0.0911.0000.8770.8860.134
남-0.3910.8771.0000.5830.173
여0.1980.8860.5831.0000.157
행정기관0.0000.1340.1730.1571.000

Missing values

2024-04-06T17:32:30.931868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:32:31.100011image/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

행정기관연령계남여
0도계읍4110
1도계읍17202
2도계읍181839291
3도계읍19277129148
4도계읍20337149188
5도계읍2124998151
6도계읍22229108121
7도계읍231529656
8도계읍241399544
9도계읍25734627
행정기관연령계남여
938성내동92606
939성내동93505
940성내동94101
941성내동95101
942성내동96413
943성내동97101
944성내동98101
945성내동99101
946성내동101101
947성내동103101