Overview

Dataset statistics

Number of variables14
Number of observations318
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.3 KiB
Average record size in memory123.4 B

Variable types

Categorical2
Text1
Numeric11

Dataset

Description인천광역시 소재 주민등록인구의 (군구명,행정구역(동읍면),항목(남자,여자인구수),10세미만,10대,20대,30대...100세이상)으로 구성된 데이터입니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15091499&srcSe=7661IVAWM27C61E190

Alerts

10세미만 is highly overall correlated with 10대 and 6 other fieldsHigh correlation
10대 is highly overall correlated with 10세미만 and 7 other fieldsHigh correlation
20대 is highly overall correlated with 10세미만 and 7 other fieldsHigh correlation
30대 is highly overall correlated with 10세미만 and 6 other fieldsHigh correlation
40대 is highly overall correlated with 10세미만 and 7 other fieldsHigh correlation
50대 is highly overall correlated with 10세미만 and 7 other fieldsHigh correlation
60대 is highly overall correlated with 10세미만 and 8 other fieldsHigh correlation
70대 is highly overall correlated with 10세미만 and 8 other fieldsHigh correlation
80대 is highly overall correlated with 10대 and 7 other fieldsHigh correlation
90대 is highly overall correlated with 60대 and 4 other fieldsHigh correlation
100세이상 is highly overall correlated with 90대High correlation
항목 is highly overall correlated with 80대 and 1 other fieldsHigh correlation
100세이상 has 111 (34.9%) zerosZeros

Reproduction

Analysis started2024-04-17 17:29:56.399231
Analysis finished2024-04-17 17:30:05.942571
Duration9.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

군구명
Categorical

Distinct10
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
서구
46 
부평구
44 
미추홀구
42 
남동구
40 
연수구
30 
Other values (5)
116 

Length

Max length4
Median length3
Mean length2.8490566
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서구 46
14.5%
부평구 44
13.8%
미추홀구 42
13.2%
남동구 40
12.6%
연수구 30
9.4%
강화군 28
8.8%
계양구 24
7.5%
중구 22
6.9%
동구 22
6.9%
옹진군 20
6.3%

Length

2024-04-18T02:30:05.999999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T02:30:06.101949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구 46
14.5%
부평구 44
13.8%
미추홀구 42
13.2%
남동구 40
12.6%
연수구 30
9.4%
강화군 28
8.8%
계양구 24
7.5%
중구 22
6.9%
동구 22
6.9%
옹진군 20
6.3%
Distinct159
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-04-18T02:30:06.355789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.9685535
Min length3

Characters and Unicode

Total characters1262
Distinct characters115
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연안동
2nd row연안동
3rd row신포동
4th row신포동
5th row신흥동
ValueCountFrequency (%)
연안동 2
 
0.6%
효성1동 2
 
0.6%
계산1동 2
 
0.6%
계산2동 2
 
0.6%
계산3동 2
 
0.6%
계산4동 2
 
0.6%
작전1동 2
 
0.6%
작전2동 2
 
0.6%
작전서운동 2
 
0.6%
효성2동 2
 
0.6%
Other values (149) 298
93.7%
2024-04-18T02:30:06.700327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
280
22.2%
1 64
 
5.1%
2 64
 
5.1%
46
 
3.6%
3 42
 
3.3%
30
 
2.4%
26
 
2.1%
26
 
2.1%
4 24
 
1.9%
24
 
1.9%
Other values (105) 636
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1032
81.8%
Decimal Number 218
 
17.3%
Other Punctuation 12
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
280
27.1%
46
 
4.5%
30
 
2.9%
26
 
2.5%
26
 
2.5%
24
 
2.3%
20
 
1.9%
18
 
1.7%
18
 
1.7%
18
 
1.7%
Other values (96) 526
51.0%
Decimal Number
ValueCountFrequency (%)
1 64
29.4%
2 64
29.4%
3 42
19.3%
4 24
 
11.0%
5 12
 
5.5%
6 8
 
3.7%
8 2
 
0.9%
7 2
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1032
81.8%
Common 230
 
18.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
280
27.1%
46
 
4.5%
30
 
2.9%
26
 
2.5%
26
 
2.5%
24
 
2.3%
20
 
1.9%
18
 
1.7%
18
 
1.7%
18
 
1.7%
Other values (96) 526
51.0%
Common
ValueCountFrequency (%)
1 64
27.8%
2 64
27.8%
3 42
18.3%
4 24
 
10.4%
5 12
 
5.2%
. 12
 
5.2%
6 8
 
3.5%
8 2
 
0.9%
7 2
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1032
81.8%
ASCII 230
 
18.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
280
27.1%
46
 
4.5%
30
 
2.9%
26
 
2.5%
26
 
2.5%
24
 
2.3%
20
 
1.9%
18
 
1.7%
18
 
1.7%
18
 
1.7%
Other values (96) 526
51.0%
ASCII
ValueCountFrequency (%)
1 64
27.8%
2 64
27.8%
3 42
18.3%
4 24
 
10.4%
5 12
 
5.2%
. 12
 
5.2%
6 8
 
3.5%
8 2
 
0.9%
7 2
 
0.9%

항목
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
남자인구수 (명)
159 
여자인구수 (명)
159 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남자인구수 (명)
2nd row여자인구수 (명)
3rd row남자인구수 (명)
4th row여자인구수 (명)
5th row남자인구수 (명)

Common Values

ValueCountFrequency (%)
남자인구수 (명) 159
50.0%
여자인구수 (명) 159
50.0%

Length

2024-04-18T02:30:06.801880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T02:30:06.876299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
318
50.0%
남자인구수 159
25.0%
여자인구수 159
25.0%

10세미만
Real number (ℝ)

HIGH CORRELATION 

Distinct276
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean692.56604
Minimum0
Maximum3500
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-18T02:30:06.956111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23
Q1209
median532.5
Q3893.5
95-th percentile2108
Maximum3500
Range3500
Interquartile range (IQR)684.5

Descriptive statistics

Standard deviation692.83039
Coefficient of variation (CV)1.0003817
Kurtosis3.9960897
Mean692.56604
Median Absolute Deviation (MAD)339
Skewness1.8669227
Sum220236
Variance480013.94
MonotonicityNot monotonic
2024-04-18T02:30:07.058483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 4
 
1.3%
233 3
 
0.9%
401 3
 
0.9%
1120 3
 
0.9%
410 3
 
0.9%
501 2
 
0.6%
150 2
 
0.6%
43 2
 
0.6%
559 2
 
0.6%
46 2
 
0.6%
Other values (266) 292
91.8%
ValueCountFrequency (%)
0 1
0.3%
2 1
0.3%
3 2
0.6%
4 2
0.6%
5 1
0.3%
7 1
0.3%
9 1
0.3%
10 1
0.3%
14 2
0.6%
16 1
0.3%
ValueCountFrequency (%)
3500 1
0.3%
3412 1
0.3%
3370 1
0.3%
3314 1
0.3%
3292 1
0.3%
3286 1
0.3%
3129 1
0.3%
3061 1
0.3%
2926 1
0.3%
2762 1
0.3%

10대
Real number (ℝ)

HIGH CORRELATION 

Distinct286
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean853.99686
Minimum1
Maximum3376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-18T02:30:07.176537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35.7
Q1289
median675
Q31284.25
95-th percentile2216.7
Maximum3376
Range3375
Interquartile range (IQR)995.25

Descriptive statistics

Standard deviation724.66448
Coefficient of variation (CV)0.84855638
Kurtosis1.0372906
Mean853.99686
Median Absolute Deviation (MAD)459.5
Skewness1.1168145
Sum271571
Variance525138.61
MonotonicityNot monotonic
2024-04-18T02:30:07.281955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 3
 
0.9%
53 3
 
0.9%
873 2
 
0.6%
1457 2
 
0.6%
424 2
 
0.6%
67 2
 
0.6%
50 2
 
0.6%
607 2
 
0.6%
431 2
 
0.6%
932 2
 
0.6%
Other values (276) 296
93.1%
ValueCountFrequency (%)
1 2
0.6%
2 1
 
0.3%
7 3
0.9%
10 2
0.6%
13 1
 
0.3%
14 2
0.6%
16 1
 
0.3%
21 1
 
0.3%
25 1
 
0.3%
29 1
 
0.3%
ValueCountFrequency (%)
3376 1
0.3%
3263 1
0.3%
3199 1
0.3%
3109 1
0.3%
3096 1
0.3%
2970 1
0.3%
2949 1
0.3%
2933 1
0.3%
2818 1
0.3%
2679 1
0.3%

20대
Real number (ℝ)

HIGH CORRELATION 

Distinct296
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1236.6541
Minimum3
Maximum3988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-18T02:30:07.401767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile48.7
Q1406.5
median1255
Q31780.5
95-th percentile2875.7
Maximum3988
Range3985
Interquartile range (IQR)1374

Descriptive statistics

Standard deviation867.25503
Coefficient of variation (CV)0.70129152
Kurtosis-0.12688925
Mean1236.6541
Median Absolute Deviation (MAD)632
Skewness0.51684446
Sum393256
Variance752131.28
MonotonicityNot monotonic
2024-04-18T02:30:07.519703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
361 2
 
0.6%
1991 2
 
0.6%
298 2
 
0.6%
91 2
 
0.6%
105 2
 
0.6%
1936 2
 
0.6%
900 2
 
0.6%
2044 2
 
0.6%
1856 2
 
0.6%
1260 2
 
0.6%
Other values (286) 298
93.7%
ValueCountFrequency (%)
3 1
0.3%
4 1
0.3%
6 1
0.3%
10 2
0.6%
12 2
0.6%
20 1
0.3%
31 1
0.3%
34 2
0.6%
36 1
0.3%
38 1
0.3%
ValueCountFrequency (%)
3988 1
0.3%
3655 1
0.3%
3610 1
0.3%
3453 1
0.3%
3446 1
0.3%
3370 1
0.3%
3252 1
0.3%
3223 1
0.3%
3182 1
0.3%
3172 1
0.3%

30대
Real number (ℝ)

HIGH CORRELATION 

Distinct302
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1259.5881
Minimum2
Maximum4897
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-18T02:30:07.635821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile38.7
Q1417.25
median1139.5
Q31747
95-th percentile3082.8
Maximum4897
Range4895
Interquartile range (IQR)1329.75

Descriptive statistics

Standard deviation990.26324
Coefficient of variation (CV)0.78618024
Kurtosis1.1579025
Mean1259.5881
Median Absolute Deviation (MAD)675.5
Skewness1.0436631
Sum400549
Variance980621.28
MonotonicityNot monotonic
2024-04-18T02:30:07.770400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1173 2
 
0.6%
389 2
 
0.6%
809 2
 
0.6%
1705 2
 
0.6%
36 2
 
0.6%
90 2
 
0.6%
10 2
 
0.6%
1335 2
 
0.6%
717 2
 
0.6%
650 2
 
0.6%
Other values (292) 298
93.7%
ValueCountFrequency (%)
2 1
0.3%
3 1
0.3%
6 1
0.3%
7 2
0.6%
10 2
0.6%
14 1
0.3%
15 1
0.3%
20 1
0.3%
25 1
0.3%
31 1
0.3%
ValueCountFrequency (%)
4897 1
0.3%
4635 1
0.3%
4419 1
0.3%
4288 1
0.3%
4258 1
0.3%
4225 1
0.3%
4134 1
0.3%
4101 1
0.3%
3879 1
0.3%
3712 1
0.3%

40대
Real number (ℝ)

HIGH CORRELATION 

Distinct301
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1510.1226
Minimum2
Maximum5162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-18T02:30:07.884240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile64.55
Q1563
median1304
Q32189.75
95-th percentile3805.3
Maximum5162
Range5160
Interquartile range (IQR)1626.75

Descriptive statistics

Standard deviation1149.7379
Coefficient of variation (CV)0.76135396
Kurtosis0.46570131
Mean1510.1226
Median Absolute Deviation (MAD)820
Skewness0.86909215
Sum480219
Variance1321897.1
MonotonicityNot monotonic
2024-04-18T02:30:08.016533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 2
 
0.6%
1221 2
 
0.6%
65 2
 
0.6%
1514 2
 
0.6%
135 2
 
0.6%
114 2
 
0.6%
1083 2
 
0.6%
1249 2
 
0.6%
1063 2
 
0.6%
1412 2
 
0.6%
Other values (291) 298
93.7%
ValueCountFrequency (%)
2 1
0.3%
3 1
0.3%
7 1
0.3%
10 1
0.3%
14 1
0.3%
15 1
0.3%
16 2
0.6%
27 1
0.3%
39 1
0.3%
49 1
0.3%
ValueCountFrequency (%)
5162 1
0.3%
5086 1
0.3%
5038 1
0.3%
5004 1
0.3%
4902 1
0.3%
4556 1
0.3%
4535 1
0.3%
4504 1
0.3%
4477 1
0.3%
4205 1
0.3%

50대
Real number (ℝ)

HIGH CORRELATION 

Distinct305
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1599.8648
Minimum10
Maximum4262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-18T02:30:08.128171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile119.85
Q1675.75
median1627
Q32286.75
95-th percentile3233.3
Maximum4262
Range4252
Interquartile range (IQR)1611

Descriptive statistics

Standard deviation992.89988
Coefficient of variation (CV)0.62061488
Kurtosis-0.75045912
Mean1599.8648
Median Absolute Deviation (MAD)798
Skewness0.18636103
Sum508757
Variance985850.18
MonotonicityNot monotonic
2024-04-18T02:30:08.229440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1894 3
 
0.9%
1666 2
 
0.6%
2243 2
 
0.6%
1781 2
 
0.6%
646 2
 
0.6%
1906 2
 
0.6%
2054 2
 
0.6%
653 2
 
0.6%
2818 2
 
0.6%
286 2
 
0.6%
Other values (295) 297
93.4%
ValueCountFrequency (%)
10 1
0.3%
15 1
0.3%
19 1
0.3%
26 1
0.3%
27 1
0.3%
28 1
0.3%
38 1
0.3%
45 1
0.3%
76 1
0.3%
78 1
0.3%
ValueCountFrequency (%)
4262 1
0.3%
4224 1
0.3%
3948 1
0.3%
3865 1
0.3%
3578 1
0.3%
3577 1
0.3%
3570 1
0.3%
3536 1
0.3%
3513 1
0.3%
3431 1
0.3%

60대
Real number (ℝ)

HIGH CORRELATION 

Distinct290
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1230.1635
Minimum20
Maximum2996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-18T02:30:08.326429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile148.5
Q1699.75
median1264.5
Q31679.5
95-th percentile2247.1
Maximum2996
Range2976
Interquartile range (IQR)979.75

Descriptive statistics

Standard deviation651.30268
Coefficient of variation (CV)0.52944399
Kurtosis-0.68243368
Mean1230.1635
Median Absolute Deviation (MAD)495
Skewness0.021191673
Sum391192
Variance424195.18
MonotonicityNot monotonic
2024-04-18T02:30:08.426557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1383 3
 
0.9%
1063 3
 
0.9%
1588 3
 
0.9%
659 3
 
0.9%
1521 2
 
0.6%
49 2
 
0.6%
729 2
 
0.6%
857 2
 
0.6%
150 2
 
0.6%
1506 2
 
0.6%
Other values (280) 294
92.5%
ValueCountFrequency (%)
20 1
0.3%
28 1
0.3%
36 1
0.3%
44 1
0.3%
49 2
0.6%
50 1
0.3%
54 1
0.3%
101 1
0.3%
113 1
0.3%
117 1
0.3%
ValueCountFrequency (%)
2996 1
0.3%
2879 1
0.3%
2758 1
0.3%
2723 1
0.3%
2585 1
0.3%
2448 1
0.3%
2425 1
0.3%
2384 1
0.3%
2369 1
0.3%
2321 1
0.3%

70대
Real number (ℝ)

HIGH CORRELATION 

Distinct273
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean561.27987
Minimum24
Maximum1500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-18T02:30:08.525296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile79
Q1354
median550.5
Q3753.5
95-th percentile1001.8
Maximum1500
Range1476
Interquartile range (IQR)399.5

Descriptive statistics

Standard deviation280.77854
Coefficient of variation (CV)0.50024694
Kurtosis-0.14658194
Mean561.27987
Median Absolute Deviation (MAD)201
Skewness0.22093785
Sum178487
Variance78836.587
MonotonicityNot monotonic
2024-04-18T02:30:08.624411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
549 3
 
0.9%
615 3
 
0.9%
834 2
 
0.6%
28 2
 
0.6%
338 2
 
0.6%
177 2
 
0.6%
366 2
 
0.6%
469 2
 
0.6%
803 2
 
0.6%
730 2
 
0.6%
Other values (263) 296
93.1%
ValueCountFrequency (%)
24 1
0.3%
28 2
0.6%
29 2
0.6%
32 1
0.3%
35 1
0.3%
39 1
0.3%
42 1
0.3%
52 1
0.3%
62 1
0.3%
66 1
0.3%
ValueCountFrequency (%)
1500 1
0.3%
1374 1
0.3%
1282 1
0.3%
1229 1
0.3%
1217 1
0.3%
1208 1
0.3%
1192 1
0.3%
1156 1
0.3%
1099 1
0.3%
1098 1
0.3%

80대
Real number (ℝ)

HIGH CORRELATION 

Distinct251
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.67296
Minimum6
Maximum853
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-18T02:30:08.734442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile35.85
Q1148.5
median221.5
Q3334
95-th percentile538.9
Maximum853
Range847
Interquartile range (IQR)185.5

Descriptive statistics

Standard deviation154.30506
Coefficient of variation (CV)0.60589494
Kurtosis1.1922801
Mean254.67296
Median Absolute Deviation (MAD)90.5
Skewness0.95569042
Sum80986
Variance23810.05
MonotonicityNot monotonic
2024-04-18T02:30:08.829993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211 4
 
1.3%
301 3
 
0.9%
29 3
 
0.9%
163 3
 
0.9%
193 3
 
0.9%
215 3
 
0.9%
264 3
 
0.9%
188 3
 
0.9%
162 3
 
0.9%
210 3
 
0.9%
Other values (241) 287
90.3%
ValueCountFrequency (%)
6 1
 
0.3%
9 2
0.6%
11 1
 
0.3%
14 1
 
0.3%
15 1
 
0.3%
16 1
 
0.3%
20 1
 
0.3%
22 1
 
0.3%
23 1
 
0.3%
29 3
0.9%
ValueCountFrequency (%)
853 1
0.3%
772 1
0.3%
769 1
0.3%
767 1
0.3%
752 1
0.3%
690 1
0.3%
680 1
0.3%
635 1
0.3%
608 1
0.3%
582 1
0.3%

90대
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.707547
Minimum0
Maximum155
Zeros2
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-18T02:30:08.932096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q116
median25
Q363.75
95-th percentile101
Maximum155
Range155
Interquartile range (IQR)47.75

Descriptive statistics

Standard deviation32.363267
Coefficient of variation (CV)0.81504071
Kurtosis0.44703155
Mean39.707547
Median Absolute Deviation (MAD)16
Skewness1.0641213
Sum12627
Variance1047.3811
MonotonicityNot monotonic
2024-04-18T02:30:09.036688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 13
 
4.1%
25 12
 
3.8%
23 11
 
3.5%
17 10
 
3.1%
18 10
 
3.1%
8 9
 
2.8%
13 8
 
2.5%
20 8
 
2.5%
12 7
 
2.2%
14 7
 
2.2%
Other values (93) 223
70.1%
ValueCountFrequency (%)
0 2
 
0.6%
1 1
 
0.3%
2 3
 
0.9%
3 4
1.3%
4 2
 
0.6%
5 6
1.9%
6 4
1.3%
7 5
1.6%
8 9
2.8%
9 6
1.9%
ValueCountFrequency (%)
155 1
0.3%
147 1
0.3%
133 1
0.3%
131 1
0.3%
130 1
0.3%
129 1
0.3%
124 2
0.6%
123 1
0.3%
109 1
0.3%
108 1
0.3%

100세이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7264151
Minimum0
Maximum12
Zeros111
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-18T02:30:09.128596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum12
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0752394
Coefficient of variation (CV)1.2020512
Kurtosis4.674008
Mean1.7264151
Median Absolute Deviation (MAD)1
Skewness1.8953628
Sum549
Variance4.3066187
MonotonicityNot monotonic
2024-04-18T02:30:09.212511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 111
34.9%
1 74
23.3%
2 49
15.4%
3 37
 
11.6%
4 21
 
6.6%
5 7
 
2.2%
6 6
 
1.9%
7 4
 
1.3%
8 4
 
1.3%
9 3
 
0.9%
ValueCountFrequency (%)
0 111
34.9%
1 74
23.3%
2 49
15.4%
3 37
 
11.6%
4 21
 
6.6%
5 7
 
2.2%
6 6
 
1.9%
7 4
 
1.3%
8 4
 
1.3%
9 3
 
0.9%
ValueCountFrequency (%)
12 2
 
0.6%
9 3
 
0.9%
8 4
 
1.3%
7 4
 
1.3%
6 6
 
1.9%
5 7
 
2.2%
4 21
 
6.6%
3 37
11.6%
2 49
15.4%
1 74
23.3%

Interactions

2024-04-18T02:30:04.720258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:56.880499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:57.644852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:58.626964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:59.403012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:00.122068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:00.838867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:01.580967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:02.518199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:03.251238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:03.949126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:04.787407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:56.953090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:57.716389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:58.698258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:59.483572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:00.187545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:00.912404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:01.649130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:02.587017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:03.315797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:04.019897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:04.871077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:57.027259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:58.029545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:58.768363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:59.554765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:00.254764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:00.977102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:01.714805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:02.657070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:03.380484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:04.093084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:04.962265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:57.099555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:58.098803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:58.843535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:59.621351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:00.321795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:01.070978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:01.783752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:02.727558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:03.448580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:04.166838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:05.023566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:57.164855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:58.161372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:58.918171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:59.681832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:00.383771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:01.152516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:01.845348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:02.790428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:03.506552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:04.232011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:05.086474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:57.228129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:58.223256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:58.989653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:59.742134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:00.443294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:01.209444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:02.131714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:02.853531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:03.575386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:04.295525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:05.146792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:57.288525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:58.285243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:59.051267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:59.798846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:00.514971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:01.263641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:02.189378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:02.914641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:03.641863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:04.365259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:05.219194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:57.362453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:58.350010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:59.119414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:59.859668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:00.584715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:01.324414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:02.256295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:02.980785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:03.704149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:04.438481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:05.308620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:57.444018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:58.427167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:59.186972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:59.925539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:00.648751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:01.387602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:02.322759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:03.049816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:03.767667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:04.514710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:05.366557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:57.506406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:58.488380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:59.249697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:59.982444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:00.706089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:01.445642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:02.382008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:03.108853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:03.821086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:04.581425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:05.439018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:57.577558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:58.560550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:29:59.328314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:00.053084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:00.773037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:01.519775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:02.452259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:03.184903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:03.887632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:30:04.653403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T02:30:09.282356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
군구명항목10세미만10대20대30대40대50대60대70대80대90대100세이상
군구명1.0000.0000.6680.7390.7440.7340.7520.7950.7770.7100.5670.3450.203
항목0.0001.0000.0000.0000.0000.0940.0000.0000.0000.1590.7430.9440.458
10세미만0.6680.0001.0000.9090.8310.9170.9380.8260.7450.6240.5040.3790.250
10대0.7390.0000.9091.0000.8850.8950.9420.8890.8210.7000.5470.4630.045
20대0.7440.0000.8310.8851.0000.8960.8980.9290.8720.7350.6200.4600.223
30대0.7340.0940.9170.8950.8961.0000.9270.8800.8160.6780.5230.5030.204
40대0.7520.0000.9380.9420.8980.9271.0000.8900.8270.7160.5990.4210.000
50대0.7950.0000.8260.8890.9290.8800.8901.0000.9370.8100.6820.6050.258
60대0.7770.0000.7450.8210.8720.8160.8270.9371.0000.8990.7870.6700.327
70대0.7100.1590.6240.7000.7350.6780.7160.8100.8991.0000.8820.7940.455
80대0.5670.7430.5040.5470.6200.5230.5990.6820.7870.8821.0000.8950.513
90대0.3450.9440.3790.4630.4600.5030.4210.6050.6700.7940.8951.0000.582
100세이상0.2030.4580.2500.0450.2230.2040.0000.2580.3270.4550.5130.5821.000
2024-04-18T02:30:09.630611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
항목군구명
항목1.0000.000
군구명0.0001.000
2024-04-18T02:30:09.699034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
10세미만10대20대30대40대50대60대70대80대90대100세이상군구명항목
10세미만1.0000.9690.9130.9550.9750.9100.7880.6310.4760.3450.1570.2610.000
10대0.9691.0000.9240.9270.9810.9410.8110.6550.5020.3650.1410.3110.000
20대0.9130.9241.0000.9610.9510.9590.8700.7140.5360.3660.1710.3150.000
30대0.9550.9270.9611.0000.9680.9280.8300.6660.4850.3270.1860.3070.071
40대0.9750.9810.9510.9681.0000.9510.8280.6790.5100.3530.1660.3210.000
50대0.9100.9410.9590.9280.9511.0000.9190.7550.6120.4490.2190.3590.000
60대0.7880.8110.8700.8300.8280.9191.0000.8990.7190.5240.2760.3430.000
70대0.6310.6550.7140.6660.6790.7550.8991.0000.8630.6440.3640.2890.120
80대0.4760.5020.5360.4850.5100.6120.7190.8631.0000.8770.4890.2050.576
90대0.3450.3650.3660.3270.3530.4490.5240.6440.8771.0000.5380.1100.774
100세이상0.1570.1410.1710.1860.1660.2190.2760.3640.4890.5381.0000.0860.450
군구명0.2610.3110.3150.3070.3210.3590.3430.2890.2050.1100.0861.0000.000
항목0.0000.0000.0000.0710.0000.0000.0000.1200.5760.7740.4500.0001.000

Missing values

2024-04-18T02:30:05.536503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T02:30:05.892756image/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

군구명행정구역(동읍면)별항목10세미만10대20대30대40대50대60대70대80대90대100세이상
0중구연안동남자인구수 (명)120151361369481737694298107130
1중구연안동여자인구수 (명)102173260235321525634338194412
2중구신포동남자인구수 (명)133178294283429462417275113120
3중구신포동여자인구수 (명)106166242227359399410354200442
4중구신흥동남자인구수 (명)401580875831106812181018524192140
5중구신흥동여자인구수 (명)39654273171493410461044655322640
6중구도원동남자인구수 (명)571452491972983803642457490
7중구도원동여자인구수 (명)69112181164242330409291127251
8중구율목동남자인구수 (명)54931831662653482992014950
9중구율목동여자인구수 (명)3497149112207261318249115250
군구명행정구역(동읍면)별항목10세미만10대20대30대40대50대60대70대80대90대100세이상
308옹진군덕적면남자인구수 (명)19428352942142741646870
309옹진군덕적면여자인구수 (명)2339553556131214177104331
310옹진군영흥면남자인구수 (명)150232386382487701796373116111
311옹진군영흥면여자인구수 (명)143169191226342550659350181441
312옹진군자월면남자인구수 (명)1416472560121117783130
313옹진군자월면여자인구수 (명)1014341427761137954131
314옹진군자월면이작출장소남자인구수 (명)77121516454935910
315옹진군자월면이작출장소여자인구수 (명)4101271627442923100
316옹진군연평면남자인구수 (명)4667361148184204176762960
317옹진군연평면여자인구수 (명)52469468711191546651160