Overview

Dataset statistics

Number of variables8
Number of observations931
Missing cells4
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory62.9 KiB
Average record size in memory69.1 B

Variable types

Categorical2
Numeric5
Text1

Dataset

Description대구광역시_동구_인구현황_20230531
Author대구광역시 동구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3038845&dataSetDetailId=30388451e8753cecb0fc&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 20:02:09.584572
Analysis finished2023-12-10 20:02:16.173643
Duration6.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준월
Categorical

Distinct41
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
2023-05
 
23
2021-09
 
23
2021-11
 
23
2023-03
 
23
2023-02
 
23
Other values (36)
816 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05
2nd row2023-05
3rd row2023-05
4th row2023-05
5th row2023-05

Common Values

ValueCountFrequency (%)
2023-05 23
 
2.5%
2021-09 23
 
2.5%
2021-11 23
 
2.5%
2023-03 23
 
2.5%
2023-02 23
 
2.5%
2023-01 23
 
2.5%
2022-12 23
 
2.5%
2022-01 23
 
2.5%
2022-10 23
 
2.5%
2022-11 23
 
2.5%
Other values (31) 701
75.3%

Length

2023-12-11T05:02:16.287905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-05 23
 
2.5%
2022-09 23
 
2.5%
2021-09 23
 
2.5%
2023-04 23
 
2.5%
2021-08 23
 
2.5%
2021-07 23
 
2.5%
2021-06 23
 
2.5%
2020-07 23
 
2.5%
2021-04 23
 
2.5%
2021-05 23
 
2.5%
Other values (31) 701
75.3%

동명
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
동구전체
 
36
지저동
 
28
도평동
 
28
신암2동
 
28
신암3동
 
28
Other values (43)
783 

Length

Max length8
Median length7
Mean length5.1310419
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동구전체 36
 
3.9%
지저동 28
 
3.0%
도평동 28
 
3.0%
신암2동 28
 
3.0%
신암3동 28
 
3.0%
안심2동 28
 
3.0%
안심1동 28
 
3.0%
해안동 28
 
3.0%
방촌동 28
 
3.0%
동촌동 28
 
3.0%
Other values (38) 643
69.1%

Length

2023-12-11T05:02:16.515954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
효목1동 41
 
4.4%
공산동 41
 
4.4%
신천1.2동 41
 
4.4%
신암4동 41
 
4.4%
신천3동 41
 
4.4%
신천4동 41
 
4.4%
지저동 41
 
4.4%
효목2동 41
 
4.4%
신암1동 41
 
4.4%
동촌동 41
 
4.4%
Other values (16) 521
56.0%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct834
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13857.596
Minimum2024
Maximum160454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 KiB
2023-12-11T05:02:16.747658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2024
5-th percentile3149
Q14975
median6984
Q38534.5
95-th percentile23922.5
Maximum160454
Range158430
Interquartile range (IQR)3559.5

Descriptive statistics

Standard deviation31056.886
Coefficient of variation (CV)2.2411453
Kurtosis17.235349
Mean13857.596
Median Absolute Deviation (MAD)1811
Skewness4.3448061
Sum12901422
Variance9.6453018 × 108
MonotonicityNot monotonic
2023-12-11T05:02:17.000783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6141 4
 
0.4%
2075 4
 
0.4%
4552 4
 
0.4%
6164 3
 
0.3%
7091 3
 
0.3%
8756 3
 
0.3%
4437 3
 
0.3%
2055 3
 
0.3%
2059 2
 
0.2%
3206 2
 
0.2%
Other values (824) 900
96.7%
ValueCountFrequency (%)
2024 1
0.1%
2026 1
0.1%
2028 1
0.1%
2034 1
0.1%
2037 1
0.1%
2042 1
0.1%
2043 2
0.2%
2045 2
0.2%
2046 1
0.1%
2048 1
0.1%
ValueCountFrequency (%)
160454 1
0.1%
159865 1
0.1%
159627 1
0.1%
159585 1
0.1%
159570 1
0.1%
159565 1
0.1%
159551 1
0.1%
159541 1
0.1%
159446 1
0.1%
159422 1
0.1%

인구 총계
Real number (ℝ)

HIGH CORRELATION 

Distinct896
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30068.958
Minimum3849
Maximum344932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 KiB
2023-12-11T05:02:17.233223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3849
5-th percentile6204.5
Q110873.5
median14038
Q318858.5
95-th percentile56845.5
Maximum344932
Range341083
Interquartile range (IQR)7985

Descriptive statistics

Standard deviation67473.154
Coefficient of variation (CV)2.2439472
Kurtosis17.109578
Mean30068.958
Median Absolute Deviation (MAD)4312
Skewness4.3253024
Sum27994200
Variance4.5526265 × 109
MonotonicityNot monotonic
2023-12-11T05:02:17.496366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18557 3
 
0.3%
18792 2
 
0.2%
3969 2
 
0.2%
7435 2
 
0.2%
7355 2
 
0.2%
14684 2
 
0.2%
8329 2
 
0.2%
13005 2
 
0.2%
15098 2
 
0.2%
7716 2
 
0.2%
Other values (886) 910
97.7%
ValueCountFrequency (%)
3849 1
0.1%
3852 1
0.1%
3861 1
0.1%
3869 1
0.1%
3891 1
0.1%
3899 1
0.1%
3929 1
0.1%
3951 1
0.1%
3969 2
0.2%
3972 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 

Distinct871
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14793.957
Minimum2073
Maximum170284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 KiB
2023-12-11T05:02:17.760950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2073
5-th percentile2929
Q15321.5
median6980
Q39316
95-th percentile27949.5
Maximum170284
Range168211
Interquartile range (IQR)3994.5

Descriptive statistics

Standard deviation33194.589
Coefficient of variation (CV)2.2437938
Kurtosis17.117227
Mean14793.957
Median Absolute Deviation (MAD)2075
Skewness4.3264783
Sum13773174
Variance1.1018807 × 109
MonotonicityNot monotonic
2023-12-11T05:02:18.020962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7680 3
 
0.3%
5487 3
 
0.3%
2133 3
 
0.3%
7998 3
 
0.3%
5521 2
 
0.2%
9255 2
 
0.2%
7673 2
 
0.2%
5710 2
 
0.2%
4799 2
 
0.2%
7848 2
 
0.2%
Other values (861) 907
97.4%
ValueCountFrequency (%)
2073 1
0.1%
2076 1
0.1%
2080 1
0.1%
2087 1
0.1%
2093 1
0.1%
2095 1
0.1%
2112 1
0.1%
2113 1
0.1%
2120 1
0.1%
2124 2
0.2%
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 

Distinct861
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15275.044
Minimum1773
Maximum174648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 KiB
2023-12-11T05:02:18.250710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1773
5-th percentile3275.5
Q15535.5
median7106
Q39736.5
95-th percentile28896
Maximum174648
Range172875
Interquartile range (IQR)4201

Descriptive statistics

Standard deviation34279.117
Coefficient of variation (CV)2.2441255
Kurtosis17.101741
Mean15275.044
Median Absolute Deviation (MAD)2274
Skewness4.3240284
Sum14221066
Variance1.1750578 × 109
MonotonicityNot monotonic
2023-12-11T05:02:18.502680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6833 3
 
0.3%
7530 3
 
0.3%
6985 3
 
0.3%
4484 2
 
0.2%
5545 2
 
0.2%
5601 2
 
0.2%
3838 2
 
0.2%
7412 2
 
0.2%
9563 2
 
0.2%
8054 2
 
0.2%
Other values (851) 908
97.5%
ValueCountFrequency (%)
1773 1
0.1%
1779 1
0.1%
1781 1
0.1%
1782 1
0.1%
1798 1
0.1%
1804 1
0.1%
1817 1
0.1%
1831 1
0.1%
1844 1
0.1%
1845 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

Distinct288
Distinct (%)31.0%
Missing2
Missing (%)0.2%
Memory size7.4 KiB
2023-12-11T05:02:19.079481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.8202368
Min length1

Characters and Unicode

Total characters2620
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

Unique149 ?
Unique (%)16.0%

Sample

1st row857
2nd row-57
3rd row-2
4th row1
5th row945
ValueCountFrequency (%)
15 23
 
2.5%
8 20
 
2.2%
7 17
 
1.8%
10 16
 
1.7%
25 15
 
1.6%
31 15
 
1.6%
42 14
 
1.5%
5 14
 
1.5%
40 14
 
1.5%
30 14
 
1.5%
Other values (214) 767
82.6%
2023-12-11T05:02:19.966770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 724
27.6%
1 333
12.7%
2 266
 
10.2%
3 226
 
8.6%
4 199
 
7.6%
5 194
 
7.4%
6 156
 
6.0%
7 146
 
5.6%
8 128
 
4.9%
0 123
 
4.7%
Other values (2) 125
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1884
71.9%
Dash Punctuation 724
 
27.6%
Space Separator 12
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 333
17.7%
2 266
14.1%
3 226
12.0%
4 199
10.6%
5 194
10.3%
6 156
8.3%
7 146
7.7%
8 128
 
6.8%
0 123
 
6.5%
9 113
 
6.0%
Dash Punctuation
ValueCountFrequency (%)
- 724
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 724
27.6%
1 333
12.7%
2 266
 
10.2%
3 226
 
8.6%
4 199
 
7.6%
5 194
 
7.4%
6 156
 
6.0%
7 146
 
5.6%
8 128
 
4.9%
0 123
 
4.7%
Other values (2) 125
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 724
27.6%
1 333
12.7%
2 266
 
10.2%
3 226
 
8.6%
4 199
 
7.6%
5 194
 
7.4%
6 156
 
6.0%
7 146
 
5.6%
8 128
 
4.9%
0 123
 
4.7%
Other values (2) 125
 
4.8%

전월인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct892
Distinct (%)96.0%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean30146.161
Minimum3852
Maximum345469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 KiB
2023-12-11T05:02:20.282498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3852
5-th percentile6200.6
Q110808
median14047
Q318856
95-th percentile69329.4
Maximum345469
Range341617
Interquartile range (IQR)8048

Descriptive statistics

Standard deviation67586.992
Coefficient of variation (CV)2.2419767
Kurtosis17.034771
Mean30146.161
Median Absolute Deviation (MAD)4322
Skewness4.3154894
Sum28005784
Variance4.5680015 × 109
MonotonicityNot monotonic
2023-12-11T05:02:20.520966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18557 3
 
0.3%
7399 2
 
0.2%
15312 2
 
0.2%
11488 2
 
0.2%
11402 2
 
0.2%
15162 2
 
0.2%
6743 2
 
0.2%
11290 2
 
0.2%
21630 2
 
0.2%
18792 2
 
0.2%
Other values (882) 908
97.5%
ValueCountFrequency (%)
3852 1
0.1%
3861 1
0.1%
3869 1
0.1%
3891 1
0.1%
3899 1
0.1%
3929 1
0.1%
3951 1
0.1%
3969 2
0.2%
3972 1
0.1%
3976 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

2023-12-11T05:02:14.450366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:10.175944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:11.158754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:12.472046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:13.387715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:14.637610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:10.370311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:11.348092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:12.648753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:13.558947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:14.806492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:10.569754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:11.924139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:12.792413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:13.724914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:15.029854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:10.752064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:12.095948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:12.999824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:13.942454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:15.323365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:10.988465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:12.292470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:13.199841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:02:14.251497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T05:02:20.691866image/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
2023-12-11T05:02:20.877840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동명기준월
동명1.0000.000
기준월0.0001.000
2023-12-11T05:02:21.431420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수인구 총계남자여자전월인구수기준월동명
세대수1.0000.9820.9850.9760.9810.0000.975
인구 총계0.9821.0000.9990.9980.9990.0000.975
남자0.9850.9991.0000.9950.9980.0000.975
여자0.9760.9980.9951.0000.9970.0000.975
전월인구수0.9810.9990.9980.9971.0000.0000.970
기준월0.0000.0000.0000.0000.0001.0000.000
동명0.9750.9750.9750.9750.9700.0001.000

Missing values

2023-12-11T05:02:15.587614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T05:02:15.903567image/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.
2023-12-11T05:02:16.083237image/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
02023-05동구전체160454339677166570173107857338820
12023-05신암1동4513872943574372-578786
22023-05신암2동44211008047585322-210082
32023-05신암3동49371128955385751111288
42023-05신암4동6580118765896598094510931
52023-05신암5동37937665365840072307435
62023-05신천1.2동59731223657416495-6912305
72023-05신천3동64521467972547425-514684
82023-05신천4동5092830140844217-288329
92023-05효목1동68441288763826505-11813005
기준월동명세대수인구 총계남자여자인구증감전월인구수
9212020-01도평동2024426822751993-314299
9222020-01불로봉무동10712271051334613759-9827203
9232020-01지저동49191044051865254-710447
9242020-01동촌동75591627981638116-1516294
9252020-01방촌동79461887691349742-4218918
9262020-01해안동71181566879247744-115669
9272020-01안심1동19840443372170922628-5844395
9282020-01안심2동455310469532651434610423
9292020-01안심3.4동277386939134233351584069351
9302020-01공산동64451490073227578-1114911