Overview

Dataset statistics

Number of variables13
Number of observations124
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.9 KiB
Average record size in memory115.0 B

Variable types

Numeric10
Text1
Categorical2

Dataset

Description울산광역시 구군, 읍면동 인구 이동(전입, 전출), 성별, 행정구역명, 순이동 등 인구이동에 대한 통계 현황 정보를 제공하고 있음.
Author울산광역시
URLhttps://www.data.go.kr/data/15005995/fileData.do

Alerts

행정구역코드 is highly overall correlated with 행정구역레벨High correlation
총 이동 전입 is highly overall correlated with 총 이동 전출 and 7 other fieldsHigh correlation
총 이동 전출 is highly overall correlated with 총 이동 전입 and 7 other fieldsHigh correlation
시군구내 전입 is highly overall correlated with 총 이동 전입 and 7 other fieldsHigh correlation
시군구내 전출 is highly overall correlated with 총 이동 전입 and 7 other fieldsHigh correlation
시군구간 전입 is highly overall correlated with 총 이동 전입 and 7 other fieldsHigh correlation
시군구간 전출 is highly overall correlated with 총 이동 전입 and 7 other fieldsHigh correlation
시도간 전입 is highly overall correlated with 총 이동 전입 and 7 other fieldsHigh correlation
시도간 전출 is highly overall correlated with 총 이동 전입 and 7 other fieldsHigh correlation
순이동 is highly overall correlated with 행정구역레벨High correlation
행정구역레벨 is highly overall correlated with 행정구역코드 and 9 other fieldsHigh correlation
행정구역레벨 is highly imbalanced (67.1%)Imbalance

Reproduction

Analysis started2024-03-14 09:03:20.364603
Analysis finished2024-03-14 09:03:44.776697
Duration24.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.824307 × 109
Minimum31
Maximum3.17104 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-14T18:03:44.909382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile31170
Q13.111062 × 109
median3.1140638 × 109
Q33.120056 × 109
95-th percentile3.171036 × 109
Maximum3.17104 × 109
Range3.17104 × 109
Interquartile range (IQR)8994000

Descriptive statistics

Standard deviation9.284777 × 108
Coefficient of variation (CV)0.32874532
Kurtosis5.7067878
Mean2.824307 × 109
Median Absolute Deviation (MAD)3007000
Skewness-2.7582953
Sum3.5021407 × 1011
Variance8.6207085 × 1017
MonotonicityIncreasing
2024-03-14T18:03:45.163421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 2
 
1.6%
3120057000 2
 
1.6%
3117052000 2
 
1.6%
3117053000 2
 
1.6%
3117054000 2
 
1.6%
3117055000 2
 
1.6%
3117056000 2
 
1.6%
3117058000 2
 
1.6%
3117059000 2
 
1.6%
3117060000 2
 
1.6%
Other values (52) 104
83.9%
ValueCountFrequency (%)
31 2
1.6%
31110 2
1.6%
31140 2
1.6%
31170 2
1.6%
31200 2
1.6%
31710 2
1.6%
3111051000 2
1.6%
3111052000 2
1.6%
3111053000 2
1.6%
3111054000 2
1.6%
ValueCountFrequency (%)
3171040000 2
1.6%
3171038000 2
1.6%
3171037000 2
1.6%
3171036000 2
1.6%
3171034000 2
1.6%
3171031000 2
1.6%
3171026500 2
1.6%
3171026200 2
1.6%
3171025900 2
1.6%
3171025600 2
1.6%

행정구역명
Text

Distinct62
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-14T18:03:45.998941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length12.096774
Min length5

Characters and Unicode

Total characters1500
Distinct characters77
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 row울산광역시
2nd row울산광역시
3rd row울산광역시 중구
4th row울산광역시 중구
5th row울산광역시 남구
ValueCountFrequency (%)
울산광역시 124
34.6%
남구 30
 
8.4%
중구 28
 
7.8%
울주군 26
 
7.3%
동구 20
 
5.6%
북구 18
 
5.0%
남목1동 2
 
0.6%
농소2동 2
 
0.6%
농소1동 2
 
0.6%
남목3동 2
 
0.6%
Other values (52) 104
29.1%
2024-03-14T18:03:47.033661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
234
15.6%
울 150
10.0%
산 134
8.9%
광 124
 
8.3%
역 124
 
8.3%
시 124
 
8.3%
동 114
 
7.6%
구 100
 
6.7%
남 38
 
2.5%
중 30
 
2.0%
Other values (67) 328
21.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1228
81.9%
Space Separator 234
 
15.6%
Decimal Number 38
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
울 150
12.2%
산 134
10.9%
광 124
10.1%
역 124
10.1%
시 124
10.1%
동 114
9.3%
구 100
 
8.1%
남 38
 
3.1%
중 30
 
2.4%
주 26
 
2.1%
Other values (61) 264
21.5%
Decimal Number
ValueCountFrequency (%)
1 14
36.8%
2 14
36.8%
3 6
15.8%
4 2
 
5.3%
5 2
 
5.3%
Space Separator
ValueCountFrequency (%)
234
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1228
81.9%
Common 272
 
18.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
울 150
12.2%
산 134
10.9%
광 124
10.1%
역 124
10.1%
시 124
10.1%
동 114
9.3%
구 100
 
8.1%
남 38
 
3.1%
중 30
 
2.4%
주 26
 
2.1%
Other values (61) 264
21.5%
Common
ValueCountFrequency (%)
234
86.0%
1 14
 
5.1%
2 14
 
5.1%
3 6
 
2.2%
4 2
 
0.7%
5 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1228
81.9%
ASCII 272
 
18.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
234
86.0%
1 14
 
5.1%
2 14
 
5.1%
3 6
 
2.2%
4 2
 
0.7%
5 2
 
0.7%
Hangul
ValueCountFrequency (%)
울 150
12.2%
산 134
10.9%
광 124
10.1%
역 124
10.1%
시 124
10.1%
동 114
9.3%
구 100
 
8.1%
남 38
 
3.1%
중 30
 
2.4%
주 26
 
2.1%
Other values (61) 264
21.5%

행정구역레벨
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
읍면동
112 
시군구
 
10
시도
 
2

Length

Max length3
Median length3
Mean length2.983871
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row시도
2nd row시도
3rd row시군구
4th row시군구
5th row시군구

Common Values

ValueCountFrequency (%)
읍면동 112
90.3%
시군구 10
 
8.1%
시도 2
 
1.6%

Length

2024-03-14T18:03:47.264290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:03:47.443291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
읍면동 112
90.3%
시군구 10
 
8.1%
시도 2
 
1.6%

성별
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
남자
62 
여자
62 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남자
2nd row여자
3rd row남자
4th row여자
5th row남자

Common Values

ValueCountFrequency (%)
남자 62
50.0%
여자 62
50.0%

Length

2024-03-14T18:03:47.630407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:03:47.900527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남자 62
50.0%
여자 62
50.0%

총 이동 전입
Real number (ℝ)

HIGH CORRELATION 

Distinct123
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2606.7823
Minimum73
Maximum57671
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-14T18:03:48.095819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile209.7
Q1552.25
median859
Q31607.25
95-th percentile9416.8
Maximum57671
Range57598
Interquartile range (IQR)1055

Descriptive statistics

Standard deviation7241.3511
Coefficient of variation (CV)2.7778888
Kurtosis41.646739
Mean2606.7823
Median Absolute Deviation (MAD)404
Skewness6.1598603
Sum323241
Variance52437166
MonotonicityNot monotonic
2024-03-14T18:03:48.340667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1642 2
 
1.6%
57671 1
 
0.8%
264 1
 
0.8%
780 1
 
0.8%
797 1
 
0.8%
1211 1
 
0.8%
1169 1
 
0.8%
1801 1
 
0.8%
1812 1
 
0.8%
1412 1
 
0.8%
Other values (113) 113
91.1%
ValueCountFrequency (%)
73 1
0.8%
75 1
0.8%
116 1
0.8%
137 1
0.8%
150 1
0.8%
151 1
0.8%
204 1
0.8%
242 1
0.8%
264 1
0.8%
280 1
0.8%
ValueCountFrequency (%)
57671 1
0.8%
50076 1
0.8%
19007 1
0.8%
16933 1
0.8%
11564 1
0.8%
10511 1
0.8%
9439 1
0.8%
9291 1
0.8%
9158 1
0.8%
8490 1
0.8%

총 이동 전출
Real number (ℝ)

HIGH CORRELATION 

Distinct119
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2837.4919
Minimum56
Maximum62637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-14T18:03:48.586669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile338.9
Q1656.75
median997.5
Q31531.25
95-th percentile10925.8
Maximum62637
Range62581
Interquartile range (IQR)874.5

Descriptive statistics

Standard deviation7869.6477
Coefficient of variation (CV)2.773452
Kurtosis41.820686
Mean2837.4919
Median Absolute Deviation (MAD)477.5
Skewness6.1768268
Sum351849
Variance61931354
MonotonicityNot monotonic
2024-03-14T18:03:48.830456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
866 3
 
2.4%
1366 2
 
1.6%
1510 2
 
1.6%
840 2
 
1.6%
399 1
 
0.8%
1394 1
 
0.8%
1535 1
 
0.8%
1689 1
 
0.8%
1767 1
 
0.8%
1520 1
 
0.8%
Other values (109) 109
87.9%
ValueCountFrequency (%)
56 1
0.8%
84 1
0.8%
116 1
0.8%
123 1
0.8%
177 1
0.8%
192 1
0.8%
335 1
0.8%
361 1
0.8%
369 1
0.8%
379 1
0.8%
ValueCountFrequency (%)
62637 1
0.8%
54646 1
0.8%
20464 1
0.8%
18322 1
0.8%
11474 1
0.8%
11323 1
0.8%
11008 1
0.8%
10460 1
0.8%
9635 1
0.8%
9213 1
0.8%

시군구내 전입
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean855.12097
Minimum23
Maximum18364
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-14T18:03:49.166001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile54.75
Q1190.75
median295
Q3450.25
95-th percentile2760.9
Maximum18364
Range18341
Interquartile range (IQR)259.5

Descriptive statistics

Standard deviation2397.7333
Coefficient of variation (CV)2.8039698
Kurtosis39.451209
Mean855.12097
Median Absolute Deviation (MAD)137
Skewness6.0299112
Sum106035
Variance5749125.2
MonotonicityNot monotonic
2024-03-14T18:03:49.416665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 2
 
1.6%
248 2
 
1.6%
295 2
 
1.6%
991 2
 
1.6%
25 2
 
1.6%
755 2
 
1.6%
127 2
 
1.6%
59 2
 
1.6%
333 2
 
1.6%
212 2
 
1.6%
Other values (103) 104
83.9%
ValueCountFrequency (%)
23 1
0.8%
25 2
1.6%
44 1
0.8%
50 1
0.8%
52 1
0.8%
54 1
0.8%
59 2
1.6%
75 1
0.8%
89 1
0.8%
94 1
0.8%
ValueCountFrequency (%)
18364 1
0.8%
16981 1
0.8%
7275 1
0.8%
6800 1
0.8%
2870 1
0.8%
2767 1
0.8%
2763 1
0.8%
2749 1
0.8%
2703 1
0.8%
2515 1
0.8%

시군구내 전출
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean855.12097
Minimum13
Maximum18364
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-14T18:03:49.666025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile54.4
Q1197.75
median305
Q3504.25
95-th percentile2760.9
Maximum18364
Range18351
Interquartile range (IQR)306.5

Descriptive statistics

Standard deviation2395.4505
Coefficient of variation (CV)2.8013002
Kurtosis39.613231
Mean855.12097
Median Absolute Deviation (MAD)130
Skewness6.0480083
Sum106035
Variance5738183.1
MonotonicityNot monotonic
2024-03-14T18:03:49.916865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
272 2
 
1.6%
281 2
 
1.6%
246 2
 
1.6%
212 2
 
1.6%
52 2
 
1.6%
150 2
 
1.6%
170 2
 
1.6%
482 2
 
1.6%
406 2
 
1.6%
315 2
 
1.6%
Other values (104) 104
83.9%
ValueCountFrequency (%)
13 1
0.8%
25 1
0.8%
39 1
0.8%
44 1
0.8%
46 1
0.8%
52 2
1.6%
68 1
0.8%
69 1
0.8%
82 1
0.8%
99 1
0.8%
ValueCountFrequency (%)
18364 1
0.8%
16981 1
0.8%
7275 1
0.8%
6800 1
0.8%
2870 1
0.8%
2767 1
0.8%
2763 1
0.8%
2749 1
0.8%
2703 1
0.8%
2515 1
0.8%

시군구간 전입
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean866.56452
Minimum16
Maximum18763
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-14T18:03:50.256956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile55.5
Q1155.25
median309
Q3502.75
95-th percentile3652.4
Maximum18763
Range18747
Interquartile range (IQR)347.5

Descriptive statistics

Standard deviation2405.1306
Coefficient of variation (CV)2.7754778
Kurtosis40.965418
Mean866.56452
Median Absolute Deviation (MAD)157.5
Skewness6.1070289
Sum107454
Variance5784653.2
MonotonicityNot monotonic
2024-03-14T18:03:50.694688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
156 2
 
1.6%
449 2
 
1.6%
344 2
 
1.6%
346 2
 
1.6%
195 1
 
0.8%
840 1
 
0.8%
323 1
 
0.8%
422 1
 
0.8%
405 1
 
0.8%
602 1
 
0.8%
Other values (110) 110
88.7%
ValueCountFrequency (%)
16 1
0.8%
19 1
0.8%
45 1
0.8%
48 1
0.8%
49 1
0.8%
52 1
0.8%
54 1
0.8%
64 1
0.8%
79 1
0.8%
83 1
0.8%
ValueCountFrequency (%)
18763 1
0.8%
17055 1
0.8%
5913 1
0.8%
5287 1
0.8%
3805 1
0.8%
3670 1
0.8%
3653 1
0.8%
3649 1
0.8%
3339 1
0.8%
3192 1
0.8%

시군구간 전출
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean866.56452
Minimum14
Maximum18763
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-14T18:03:50.942899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile70.15
Q1203.75
median314
Q3446.25
95-th percentile3374.95
Maximum18763
Range18749
Interquartile range (IQR)242.5

Descriptive statistics

Standard deviation2400.9326
Coefficient of variation (CV)2.7706335
Kurtosis41.258378
Mean866.56452
Median Absolute Deviation (MAD)128
Skewness6.1348943
Sum107454
Variance5764477.5
MonotonicityNot monotonic
2024-03-14T18:03:51.196758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
209 3
 
2.4%
71 2
 
1.6%
411 2
 
1.6%
425 2
 
1.6%
432 2
 
1.6%
219 2
 
1.6%
85 2
 
1.6%
241 2
 
1.6%
442 2
 
1.6%
372 2
 
1.6%
Other values (103) 103
83.1%
ValueCountFrequency (%)
14 1
0.8%
19 1
0.8%
35 1
0.8%
43 1
0.8%
58 1
0.8%
65 1
0.8%
70 1
0.8%
71 2
1.6%
85 2
1.6%
93 1
0.8%
ValueCountFrequency (%)
18763 1
0.8%
17055 1
0.8%
5636 1
0.8%
5269 1
0.8%
4477 1
0.8%
4238 1
0.8%
3424 1
0.8%
3097 1
0.8%
2927 1
0.8%
2762 1
0.8%

시도간 전입
Real number (ℝ)

HIGH CORRELATION 

Distinct117
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean885.09677
Minimum31
Maximum20544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-14T18:03:51.530943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile63.7
Q1155.25
median298.5
Q3499.5
95-th percentile3623.4
Maximum20544
Range20513
Interquartile range (IQR)344.25

Descriptive statistics

Standard deviation2467.2381
Coefficient of variation (CV)2.7875348
Kurtosis43.80937
Mean885.09677
Median Absolute Deviation (MAD)162.5
Skewness6.2675066
Sum109752
Variance6087263.7
MonotonicityNot monotonic
2024-03-14T18:03:51.770415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
186 2
 
1.6%
453 2
 
1.6%
308 2
 
1.6%
136 2
 
1.6%
128 2
 
1.6%
274 2
 
1.6%
111 2
 
1.6%
84 1
 
0.8%
246 1
 
0.8%
492 1
 
0.8%
Other values (107) 107
86.3%
ValueCountFrequency (%)
31 1
0.8%
34 1
0.8%
38 1
0.8%
39 1
0.8%
46 1
0.8%
54 1
0.8%
61 1
0.8%
79 1
0.8%
81 1
0.8%
83 1
0.8%
ValueCountFrequency (%)
20544 1
0.8%
16040 1
0.8%
5819 1
0.8%
5162 1
0.8%
4846 1
0.8%
3939 1
0.8%
3732 1
0.8%
3008 1
0.8%
2852 1
0.8%
2772 1
0.8%

시도간 전출
Real number (ℝ)

HIGH CORRELATION 

Distinct117
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1115.8065
Minimum29
Maximum25510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-14T18:03:52.011400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile79.45
Q1221.75
median363
Q3667.5
95-th percentile4300.7
Maximum25510
Range25481
Interquartile range (IQR)445.75

Descriptive statistics

Standard deviation3098.6301
Coefficient of variation (CV)2.7770319
Kurtosis43.400338
Mean1115.8065
Median Absolute Deviation (MAD)192.5
Skewness6.2558065
Sum138360
Variance9601508.4
MonotonicityNot monotonic
2024-03-14T18:03:52.263706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
558 2
 
1.6%
357 2
 
1.6%
431 2
 
1.6%
224 2
 
1.6%
606 2
 
1.6%
257 2
 
1.6%
540 2
 
1.6%
765 1
 
0.8%
348 1
 
0.8%
196 1
 
0.8%
Other values (107) 107
86.3%
ValueCountFrequency (%)
29 1
0.8%
34 1
0.8%
40 1
0.8%
44 1
0.8%
66 1
0.8%
69 1
0.8%
79 1
0.8%
82 1
0.8%
109 1
0.8%
125 1
0.8%
ValueCountFrequency (%)
25510 1
0.8%
20610 1
0.8%
7553 1
0.8%
6253 1
0.8%
5628 1
0.8%
4817 1
0.8%
4358 1
0.8%
3976 1
0.8%
3806 1
0.8%
3536 1
0.8%

순이동
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-230.70968
Minimum-4966
Maximum707
Zeros1
Zeros (%)0.8%
Negative96
Negative (%)77.4%
Memory size1.2 KiB
2024-03-14T18:03:52.551900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4966
5-th percentile-1053.5
Q1-216.5
median-126
Q3-24.25
95-th percentile195
Maximum707
Range5673
Interquartile range (IQR)192.25

Descriptive statistics

Standard deviation691.29052
Coefficient of variation (CV)-2.9963655
Kurtosis29.99622
Mean-230.70968
Median Absolute Deviation (MAD)95.5
Skewness-4.9929513
Sum-28608
Variance477882.58
MonotonicityNot monotonic
2024-03-14T18:03:52.967648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9 2
 
1.6%
-55 2
 
1.6%
-228 2
 
1.6%
-152 2
 
1.6%
-183 2
 
1.6%
-112 2
 
1.6%
-116 2
 
1.6%
-71 2
 
1.6%
27 2
 
1.6%
195 2
 
1.6%
Other values (101) 104
83.9%
ValueCountFrequency (%)
-4966 1
0.8%
-4570 1
0.8%
-2032 1
0.8%
-1970 1
0.8%
-1457 1
0.8%
-1389 1
0.8%
-1070 1
0.8%
-960 1
0.8%
-566 1
0.8%
-556 1
0.8%
ValueCountFrequency (%)
707 1
0.8%
662 1
0.8%
655 1
0.8%
582 1
0.8%
245 1
0.8%
196 1
0.8%
195 2
1.6%
172 1
0.8%
159 1
0.8%
139 1
0.8%

Interactions

2024-03-14T18:03:42.516475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:21.076701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:23.628972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:26.152641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:28.529556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:30.926086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:32.637982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:35.171781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:37.738559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:40.170315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:42.665212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:21.332240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:23.884339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:26.313538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:28.787606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:31.083660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:32.882452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:35.434192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:37.986669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:40.422146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:42.909581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:21.588756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:24.134748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:26.504441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:29.046779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:31.297442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:33.135824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:35.689961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:38.229445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:40.669534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:43.160444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:21.845315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:24.387288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:26.754873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:29.301419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:31.476519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:33.390439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:35.949546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:38.477851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:40.918526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:43.315548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:22.107587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:24.646723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:27.013976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:29.560506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:31.638035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:33.649723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:36.212892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:38.725472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:41.169791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:43.468680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:22.368209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:24.903984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:27.271792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:29.817638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:31.797549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:33.910170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:36.472146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:38.974844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:41.421362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:43.627839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:22.630985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:25.163209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:27.531041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:30.075348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:31.956515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:34.169913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:36.733181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:39.221357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:41.686216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:43.788486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:22.893426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:25.426125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:27.794472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:30.272170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:32.116034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:34.431139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:36.992566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:39.472071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:41.894945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:43.923260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:23.137335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:25.663890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:28.033060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:30.414891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:32.261586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:34.671267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:37.238964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:39.699953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:42.236869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:44.061693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:23.382756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:25.907035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:28.280462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:30.584537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:32.449440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:34.924309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:37.486707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:39.935430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:03:42.374347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:03:53.244423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역코드행정구역명행정구역레벨성별총 이동 전입총 이동 전출시군구내 전입시군구내 전출시군구간 전입시군구간 전출시도간 전입시도간 전출순이동
행정구역코드1.0001.0001.0000.0001.0001.0001.0001.0000.7680.9970.8281.0000.933
행정구역명1.0001.0001.0000.0000.7770.7771.0001.0000.9050.9960.8510.8760.984
행정구역레벨1.0001.0001.0000.0001.0001.0001.0001.0000.8940.9010.9221.0000.991
성별0.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
총 이동 전입1.0000.7771.0000.0001.0001.0001.0001.0000.9240.9540.9450.9540.811
총 이동 전출1.0000.7771.0000.0001.0001.0001.0001.0000.9240.9540.9450.9540.811
시군구내 전입1.0001.0001.0000.0001.0001.0001.0001.0000.8950.9900.8990.9160.927
시군구내 전출1.0001.0001.0000.0001.0001.0001.0001.0000.8950.9900.8990.9160.927
시군구간 전입0.7680.9050.8940.0000.9240.9240.8950.8951.0000.8410.9630.9580.803
시군구간 전출0.9970.9960.9010.0000.9540.9540.9900.9900.8411.0000.8510.8360.908
시도간 전입0.8280.8510.9220.0000.9450.9450.8990.8990.9630.8511.0001.0000.895
시도간 전출1.0000.8761.0000.0000.9540.9540.9160.9160.9580.8361.0001.0000.902
순이동0.9330.9840.9910.0000.8110.8110.9270.9270.8030.9080.8950.9021.000
2024-03-14T18:03:53.571090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역레벨성별
행정구역레벨1.0000.000
성별0.0001.000
2024-03-14T18:03:53.835483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역코드총 이동 전입총 이동 전출시군구내 전입시군구내 전출시군구간 전입시군구간 전출시도간 전입시도간 전출순이동행정구역레벨성별
행정구역코드1.000-0.286-0.353-0.392-0.382-0.356-0.474-0.144-0.2200.4600.9960.000
총 이동 전입-0.2861.0000.9660.8980.8410.9490.8830.9460.961-0.2000.9880.000
총 이동 전출-0.3530.9661.0000.8800.8810.9250.9250.9140.963-0.3800.9880.000
시군구내 전입-0.3920.8980.8801.0000.9270.7980.7230.7950.837-0.2170.9960.000
시군구내 전출-0.3820.8410.8810.9271.0000.7350.7020.7590.802-0.3930.9960.000
시군구간 전입-0.3560.9490.9250.7980.7351.0000.9390.8610.892-0.1990.9350.000
시군구간 전출-0.4740.8830.9250.7230.7020.9391.0000.8150.868-0.4130.9680.000
시도간 전입-0.1440.9460.9140.7950.7590.8610.8151.0000.971-0.1990.9640.000
시도간 전출-0.2200.9610.9630.8370.8020.8920.8680.9711.000-0.3080.9920.000
순이동0.460-0.200-0.380-0.217-0.393-0.199-0.413-0.199-0.3081.0000.8710.000
행정구역레벨0.9960.9880.9880.9960.9960.9350.9680.9640.9920.8711.0000.000
성별0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-03-14T18:03:44.353857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:03:44.659979image/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

행정구역코드행정구역명행정구역레벨성별총 이동 전입총 이동 전출시군구내 전입시군구내 전출시군구간 전입시군구간 전출시도간 전입시도간 전출순이동
031울산광역시시도남자5767162637183641836418763187632054425510-4966
131울산광역시시도여자5007654646169811698117055170551604020610-4570
231110울산광역시 중구시군구남자929111323287028703649447727723976-2032
331110울산광역시 중구시군구여자849010460276327633339423823883459-1970
431140울산광역시 남구시군구남자1900720464727572755913563658197553-1457
531140울산광역시 남구시군구여자1693318322680068005287526948466253-1389
631170울산광역시 동구시군구남자72988368270327031743212928523536-1070
731170울산광역시 동구시군구여자60567016242324231567185920662734-960
831200울산광역시 북구시군구남자1051111008276727673805342439394817-497
931200울산광역시 북구시군구여자91589213248024803670292730083806-55
행정구역코드행정구역명행정구역레벨성별총 이동 전입총 이동 전출시군구내 전입시군구내 전출시군구간 전입시군구간 전출시도간 전입시도간 전출순이동
1143171034000울산광역시 울주군 웅촌면읍면동남자3964127569122119199224-16
1153171034000울산광역시 울주군 웅촌면읍면동여자45636159521238527422495
1163171036000울산광역시 울주군 두동면읍면동남자242192545210958798250
1173171036000울산광역시 울주군 두동면읍면동여자20417752469165616627
1183171037000울산광역시 울주군 두서면읍면동남자15012344445235544427
1193171037000울산광역시 울주군 두서면읍면동여자1161162539454346340
1203171038000울산광역시 울주군 상북면읍면동남자429426137161139851531803
1213171038000울산광역시 울주군 상북면읍면동여자3923791341501217113715813
1223171040000울산광역시 울주군 삼동면읍면동남자7584252516193440-9
1233171040000울산광역시 울주군 삼동면읍면동여자735623131914312917