Overview

Dataset statistics

Number of variables8
Number of observations701
Missing cells4
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.4 KiB
Average record size in memory69.2 B

Variable types

Categorical2
Numeric5
Text1

Dataset

Description대구광역시_동구_인구현황_20220731
Author대구광역시 동구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3038845&dataSetDetailId=30388451b867064fe350&provdMethod=FILE

Alerts

세대수 is highly overall correlated with 인구 총계 and 4 other fieldsHigh correlation
인구 총계 is highly overall correlated with 세대수 and 4 other fieldsHigh correlation
남자 is highly overall correlated with 세대수 and 4 other fieldsHigh correlation
여자 is highly overall correlated with 세대수 and 4 other fieldsHigh correlation
전월인구수 is highly overall correlated with 세대수 and 4 other fieldsHigh correlation
동명 is highly overall correlated with 세대수 and 4 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 18:56:50.045037
Analysis finished2023-12-10 18:56:54.802773
Duration4.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준월
Categorical

Distinct31
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2022-07
 
23
2021-04
 
23
2021-09
 
23
2021-10
 
23
2021-11
 
23
Other values (26)
586 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-07
2nd row2022-07
3rd row2022-07
4th row2022-07
5th row2022-07

Common Values

ValueCountFrequency (%)
2022-07 23
 
3.3%
2021-04 23
 
3.3%
2021-09 23
 
3.3%
2021-10 23
 
3.3%
2021-11 23
 
3.3%
2021-12 23
 
3.3%
2022-03 23
 
3.3%
2022-02 23
 
3.3%
2021-08 23
 
3.3%
2022-04 23
 
3.3%
Other values (21) 471
67.2%

Length

2023-12-11T03:56:54.913766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-07 23
 
3.3%
2022-01 23
 
3.3%
2021-03 23
 
3.3%
2021-02 23
 
3.3%
2021-01 23
 
3.3%
2020-12 23
 
3.3%
2020-09 23
 
3.3%
2020-10 23
 
3.3%
2022-06 23
 
3.3%
2020-08 23
 
3.3%
Other values (21) 471
67.2%

동명
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
신암2동
 
28
신암1동
 
28
도평동
 
28
신암3동
 
28
안심2동
 
28
Other values (43)
561 

Length

Max length8
Median length6
Mean length5.5592011
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동구전체
2nd row신암1동
3rd row신암2동
4th row신암3동
5th row신암4동

Common Values

ValueCountFrequency (%)
신암2동 28
 
4.0%
신암1동 28
 
4.0%
도평동 28
 
4.0%
신암3동 28
 
4.0%
안심2동 28
 
4.0%
안심1동 28
 
4.0%
해안동 28
 
4.0%
방촌동 28
 
4.0%
동촌동 28
 
4.0%
지저동 28
 
4.0%
Other values (38) 421
60.1%

Length

2023-12-11T03:56:55.090441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신암2동 31
 
4.4%
공산동 31
 
4.4%
신암5동 31
 
4.4%
신암4동 31
 
4.4%
신천1.2동 31
 
4.4%
신천3동 31
 
4.4%
신천4동 31
 
4.4%
신암1동 31
 
4.4%
효목2동 31
 
4.4%
불로봉무동 31
 
4.4%
Other values (16) 391
55.8%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct635
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13850.328
Minimum2024
Maximum159585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-12-11T03:56:55.275774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2024
5-th percentile3165
Q14867
median7015
Q38414
95-th percentile27819
Maximum159585
Range157561
Interquartile range (IQR)3547

Descriptive statistics

Standard deviation30990.262
Coefficient of variation (CV)2.237511
Kurtosis17.124854
Mean13850.328
Median Absolute Deviation (MAD)1820
Skewness4.3285682
Sum9709080
Variance9.6039632 × 108
MonotonicityNot monotonic
2023-12-11T03:56:55.471648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4552 4
 
0.6%
6141 4
 
0.6%
2055 3
 
0.4%
7091 3
 
0.4%
6164 3
 
0.4%
6338 2
 
0.3%
20055 2
 
0.3%
8806 2
 
0.3%
7186 2
 
0.3%
8835 2
 
0.3%
Other values (625) 674
96.1%
ValueCountFrequency (%)
2024 1
 
0.1%
2026 1
 
0.1%
2028 1
 
0.1%
2043 1
 
0.1%
2045 1
 
0.1%
2046 1
 
0.1%
2051 1
 
0.1%
2052 1
 
0.1%
2053 1
 
0.1%
2055 3
0.4%
ValueCountFrequency (%)
159585 1
0.1%
159570 1
0.1%
159446 1
0.1%
159221 1
0.1%
159077 1
0.1%
159020 1
0.1%
158708 1
0.1%
158423 1
0.1%
158045 1
0.1%
157655 1
0.1%

인구 총계
Real number (ℝ)

HIGH CORRELATION 

Distinct684
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30246.993
Minimum3972
Maximum344932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-12-11T03:56:55.710436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3972
5-th percentile6278
Q110673
median14062
Q318847
95-th percentile69391
Maximum344932
Range340960
Interquartile range (IQR)8174

Descriptive statistics

Standard deviation67774.96
Coefficient of variation (CV)2.2407173
Kurtosis16.987799
Mean30246.993
Median Absolute Deviation (MAD)4243
Skewness4.3071818
Sum21203142
Variance4.5934452 × 109
MonotonicityNot monotonic
2023-12-11T03:56:55.917904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18792 2
 
0.3%
7355 2
 
0.3%
7399 2
 
0.3%
13604 2
 
0.3%
21721 2
 
0.3%
18685 2
 
0.3%
15098 2
 
0.3%
4183 2
 
0.3%
341730 2
 
0.3%
14899 2
 
0.3%
Other values (674) 681
97.1%
ValueCountFrequency (%)
3972 1
0.1%
3976 1
0.1%
3978 1
0.1%
3986 1
0.1%
3994 1
0.1%
4000 1
0.1%
4003 1
0.1%
4005 1
0.1%
4012 1
0.1%
4044 1
0.1%
ValueCountFrequency (%)
344932 1
0.1%
343933 1
0.1%
343857 1
0.1%
343568 1
0.1%
343317 1
0.1%
343233 1
0.1%
342897 1
0.1%
342657 1
0.1%
342656 1
0.1%
342627 1
0.1%

남자
Real number (ℝ)

HIGH CORRELATION 

Distinct660
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14892.077
Minimum2113
Maximum170284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-12-11T03:56:56.123984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2113
5-th percentile2950
Q15175
median6977
Q39304
95-th percentile34230
Maximum170284
Range168171
Interquartile range (IQR)4129

Descriptive statistics

Standard deviation33366.267
Coefficient of variation (CV)2.2405382
Kurtosis16.995176
Mean14892.077
Median Absolute Deviation (MAD)2064
Skewness4.3083786
Sum10439346
Variance1.1133078 × 109
MonotonicityNot monotonic
2023-12-11T03:56:56.321731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2133 3
 
0.4%
7680 3
 
0.4%
7998 3
 
0.4%
9255 2
 
0.3%
6177 2
 
0.3%
9335 2
 
0.3%
2154 2
 
0.3%
7032 2
 
0.3%
10679 2
 
0.3%
9267 2
 
0.3%
Other values (650) 678
96.7%
ValueCountFrequency (%)
2113 1
 
0.1%
2124 1
 
0.1%
2125 1
 
0.1%
2127 1
 
0.1%
2128 1
 
0.1%
2131 1
 
0.1%
2133 3
0.4%
2154 2
0.3%
2161 1
 
0.1%
2168 1
 
0.1%
ValueCountFrequency (%)
170284 1
0.1%
169757 1
0.1%
169683 1
0.1%
169428 1
0.1%
169224 1
0.1%
169170 1
0.1%
169113 1
0.1%
168940 1
0.1%
168899 1
0.1%
168793 1
0.1%

여자
Real number (ℝ)

HIGH CORRELATION 

Distinct655
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15354.973
Minimum1847
Maximum174648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-12-11T03:56:56.529488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1847
5-th percentile3327
Q15504
median7149
Q39723
95-th percentile35158
Maximum174648
Range172801
Interquartile range (IQR)4219

Descriptive statistics

Standard deviation34409.191
Coefficient of variation (CV)2.2409151
Kurtosis16.980024
Mean15354.973
Median Absolute Deviation (MAD)2245
Skewness4.3058717
Sum10763836
Variance1.1839924 × 109
MonotonicityNot monotonic
2023-12-11T03:56:56.729642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6833 3
 
0.4%
5788 2
 
0.3%
5701 2
 
0.3%
10996 2
 
0.3%
7490 2
 
0.3%
3414 2
 
0.3%
4497 2
 
0.3%
5922 2
 
0.3%
6732 2
 
0.3%
6824 2
 
0.3%
Other values (645) 680
97.0%
ValueCountFrequency (%)
1847 1
0.1%
1848 1
0.1%
1851 1
0.1%
1863 1
0.1%
1867 1
0.1%
1872 1
0.1%
1873 1
0.1%
1879 2
0.3%
1890 1
0.1%
1898 1
0.1%
ValueCountFrequency (%)
174648 1
0.1%
174250 1
0.1%
174140 1
0.1%
174100 1
0.1%
174093 1
0.1%
174063 1
0.1%
173934 1
0.1%
173884 1
0.1%
173784 1
0.1%
173759 1
0.1%

인구증감
Text

Distinct250
Distinct (%)35.8%
Missing2
Missing (%)0.3%
Memory size5.6 KiB
2023-12-11T03:56:57.244319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.8025751
Min length1

Characters and Unicode

Total characters1959
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)18.5%

Sample

1st row-279
2nd row-24
3rd row-27
4th row-18
5th row-38
ValueCountFrequency (%)
15 18
 
2.6%
7 15
 
2.1%
8 14
 
2.0%
1 12
 
1.7%
30 12
 
1.7%
46 11
 
1.6%
10 11
 
1.6%
17 11
 
1.6%
13 11
 
1.6%
6 11
 
1.6%
Other values (183) 573
82.0%
2023-12-11T03:56:57.999908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 537
27.4%
1 257
13.1%
2 193
 
9.9%
3 168
 
8.6%
5 148
 
7.6%
4 142
 
7.2%
6 124
 
6.3%
7 110
 
5.6%
8 96
 
4.9%
0 86
 
4.4%
Other values (2) 98
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1410
72.0%
Dash Punctuation 537
 
27.4%
Space Separator 12
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 257
18.2%
2 193
13.7%
3 168
11.9%
5 148
10.5%
4 142
10.1%
6 124
8.8%
7 110
7.8%
8 96
 
6.8%
0 86
 
6.1%
9 86
 
6.1%
Dash Punctuation
ValueCountFrequency (%)
- 537
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1959
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 537
27.4%
1 257
13.1%
2 193
 
9.9%
3 168
 
8.6%
5 148
 
7.6%
4 142
 
7.2%
6 124
 
6.3%
7 110
 
5.6%
8 96
 
4.9%
0 86
 
4.4%
Other values (2) 98
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1959
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 537
27.4%
1 257
13.1%
2 193
 
9.9%
3 168
 
8.6%
5 148
 
7.6%
4 142
 
7.2%
6 124
 
6.3%
7 110
 
5.6%
8 96
 
4.9%
0 86
 
4.4%
Other values (2) 98
 
5.0%

전월인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct681
Distinct (%)97.4%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean30346.761
Minimum3976
Maximum345469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-12-11T03:56:58.221356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3976
5-th percentile6283.4
Q110641
median14113
Q318845.5
95-th percentile69392.4
Maximum345469
Range341493
Interquartile range (IQR)8204.5

Descriptive statistics

Standard deviation67917.488
Coefficient of variation (CV)2.2380473
Kurtosis16.890959
Mean30346.761
Median Absolute Deviation (MAD)4257
Skewness4.2945103
Sum21212386
Variance4.6127851 × 109
MonotonicityNot monotonic
2023-12-11T03:56:58.428637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15164 2
 
0.3%
4183 2
 
0.3%
15098 2
 
0.3%
18685 2
 
0.3%
6743 2
 
0.3%
7716 2
 
0.3%
341498 2
 
0.3%
18792 2
 
0.3%
13604 2
 
0.3%
21721 2
 
0.3%
Other values (671) 679
96.9%
ValueCountFrequency (%)
3976 1
0.1%
3978 1
0.1%
3986 1
0.1%
3994 1
0.1%
4000 1
0.1%
4003 1
0.1%
4005 1
0.1%
4012 1
0.1%
4044 1
0.1%
4052 1
0.1%
ValueCountFrequency (%)
345469 1
0.1%
344932 1
0.1%
343933 1
0.1%
343857 1
0.1%
343568 1
0.1%
343317 1
0.1%
343233 1
0.1%
342897 1
0.1%
342657 1
0.1%
342656 1
0.1%

Interactions

2023-12-11T03:56:53.712682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:50.500298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:51.222385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:52.326440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:53.029380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:53.838551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:50.639224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:51.726624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:52.476568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:53.172522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:53.997112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:50.772269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:51.878613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:52.611704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:53.289365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:54.144297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:50.931863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:52.016652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:52.750580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:53.406360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:54.264511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:51.085764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:52.161807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:52.881940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:56:53.553532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T03:56:58.561743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준월동명세대수인구 총계남자여자전월인구수
기준월1.0000.0000.0000.0000.0000.0000.000
동명0.0001.0001.0001.0001.0001.0001.000
세대수0.0001.0001.0001.0001.0001.0001.000
인구 총계0.0001.0001.0001.0001.0001.0001.000
남자0.0001.0001.0001.0001.0001.0001.000
여자0.0001.0001.0001.0001.0001.0001.000
전월인구수0.0001.0001.0001.0001.0001.0001.000
2023-12-11T03:56:59.067930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동명기준월
동명1.0000.000
기준월0.0001.000
2023-12-11T03:56:59.208250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수인구 총계남자여자전월인구수기준월동명
세대수1.0000.9840.9870.9780.9830.0000.967
인구 총계0.9841.0000.9990.9980.9980.0000.967
남자0.9870.9991.0000.9960.9970.0000.967
여자0.9780.9980.9961.0000.9970.0000.967
전월인구수0.9830.9980.9970.9971.0000.0000.960
기준월0.0000.0000.0000.0000.0001.0000.000
동명0.9670.9670.9670.9670.9600.0001.000

Missing values

2023-12-11T03:56:54.447548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T03:56:54.612733image/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.
2023-12-11T03:56:54.729783image/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

기준월동명세대수인구 총계남자여자인구증감전월인구수
02022-07동구전체159585340847167446173401-279341126
12022-07신암1동4618897444904484-248998
22022-07신암2동44611024548485397-2710272
32022-07신암3동50791154656475889-1811564
42022-07신암4동63201129056645626-3811328
52022-07신암5동316162382943329566232
62022-07신천1.2동61071268859946694-2212710
72022-07신천3동5761129866433655332912657
82022-07신천4동5138841141514260-98420
92022-07효목1동70191342866456783-4913477
기준월동명세대수인구 총계남자여자인구증감전월인구수
6912020-01도평동2024426822751993-314299
6922020-01불로봉무동10712271051334613759-9827203
6932020-01지저동49191044051865254-710447
6942020-01동촌동75591627981638116-1516294
6952020-01방촌동79461887691349742-4218918
6962020-01해안동71181566879247744-115669
6972020-01안심1동19840443372170922628-5844395
6982020-01안심2동455310469532651434610423
6992020-01안심3.4동277386939134233351584069351
7002020-01공산동64451490073227578-1114911