Overview

Dataset statistics

Number of variables14
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 KiB
Average record size in memory125.6 B

Variable types

Categorical1
Text1
Numeric11
DateTime1

Dataset

Description성남시 동별, 성별 인구에 대한 현황이며, 총인구수, 19세이상, 65세이상, 세대수, 제외국민 항목으로 구성되어 있습니다.
Author경기도 성남시
URLhttps://www.data.go.kr/data/15007386/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
인구수_계 is highly overall correlated with 인구수_남 and 9 other fieldsHigh correlation
인구수_남 is highly overall correlated with 인구수_계 and 9 other fieldsHigh correlation
인구수_여 is highly overall correlated with 인구수_계 and 9 other fieldsHigh correlation
19세 이상_계 is highly overall correlated with 인구수_계 and 9 other fieldsHigh correlation
19세 이상_남 is highly overall correlated with 인구수_계 and 9 other fieldsHigh correlation
19세 이상_여 is highly overall correlated with 인구수_계 and 9 other fieldsHigh correlation
65세 이상_계 is highly overall correlated with 인구수_계 and 9 other fieldsHigh correlation
65세 이상_남자 is highly overall correlated with 인구수_계 and 9 other fieldsHigh correlation
65세 이상_여자 is highly overall correlated with 인구수_계 and 9 other fieldsHigh correlation
세대수 is highly overall correlated with 인구수_계 and 9 other fieldsHigh correlation
재외국민 is highly overall correlated with 인구수_계 and 10 other fieldsHigh correlation
구별 is highly overall correlated with 재외국민High correlation
has unique valuesUnique
인구수_계 has unique valuesUnique
인구수_남 has unique valuesUnique
인구수_여 has unique valuesUnique
19세 이상_계 has unique valuesUnique
19세 이상_남 has unique valuesUnique
19세 이상_여 has unique valuesUnique
65세 이상_계 has unique valuesUnique
65세 이상_남자 has unique valuesUnique
65세 이상_여자 has unique valuesUnique
세대수 has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:22:29.213573
Analysis finished2024-04-29 22:22:41.889444
Duration12.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구별
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
분당구
22 
수정구
17 
중원구
11 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
분당구 22
44.0%
수정구 17
34.0%
중원구 11
22.0%

Length

2024-04-30T07:22:41.949444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:22:42.052209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
분당구 22
44.0%
수정구 17
34.0%
중원구 11
22.0%


Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2024-04-30T07:22:42.240694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.4
Min length4

Characters and Unicode

Total characters620
Distinct characters55
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

Unique50 ?
Unique (%)100.0%

Sample

1st row신흥1동
2nd row신흥2동
3rd row신흥3동
4th row태평1동
5th row태평2동
ValueCountFrequency (%)
신흥1동 1
 
2.0%
서현1동 1
 
2.0%
백현동 1
 
2.0%
도촌동 1
 
2.0%
분당동 1
 
2.0%
수내1동 1
 
2.0%
수내2동 1
 
2.0%
수내3동 1
 
2.0%
정자동 1
 
2.0%
정자1동 1
 
2.0%
Other values (40) 40
80.0%
2024-04-30T07:22:42.544569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
436
70.3%
50
 
8.1%
1 12
 
1.9%
2 11
 
1.8%
3 6
 
1.0%
5
 
0.8%
5
 
0.8%
5
 
0.8%
5
 
0.8%
4
 
0.6%
Other values (45) 81
 
13.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 436
70.3%
Other Letter 154
 
24.8%
Decimal Number 30
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
50
32.5%
5
 
3.2%
5
 
3.2%
5
 
3.2%
5
 
3.2%
4
 
2.6%
4
 
2.6%
4
 
2.6%
4
 
2.6%
4
 
2.6%
Other values (40) 64
41.6%
Decimal Number
ValueCountFrequency (%)
1 12
40.0%
2 11
36.7%
3 6
20.0%
4 1
 
3.3%
Space Separator
ValueCountFrequency (%)
436
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 466
75.2%
Hangul 154
 
24.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
50
32.5%
5
 
3.2%
5
 
3.2%
5
 
3.2%
5
 
3.2%
4
 
2.6%
4
 
2.6%
4
 
2.6%
4
 
2.6%
4
 
2.6%
Other values (40) 64
41.6%
Common
ValueCountFrequency (%)
436
93.6%
1 12
 
2.6%
2 11
 
2.4%
3 6
 
1.3%
4 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 466
75.2%
Hangul 154
 
24.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
436
93.6%
1 12
 
2.6%
2 11
 
2.4%
3 6
 
1.3%
4 1
 
0.2%
Hangul
ValueCountFrequency (%)
50
32.5%
5
 
3.2%
5
 
3.2%
5
 
3.2%
5
 
3.2%
4
 
2.6%
4
 
2.6%
4
 
2.6%
4
 
2.6%
4
 
2.6%
Other values (40) 64
41.6%

인구수_계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18389.28
Minimum3131
Maximum45309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-04-30T07:22:42.674960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3131
5-th percentile4623.9
Q112021
median16581.5
Q324829
95-th percentile31809.9
Maximum45309
Range42178
Interquartile range (IQR)12808

Descriptive statistics

Standard deviation8911.6796
Coefficient of variation (CV)0.48461275
Kurtosis0.32611435
Mean18389.28
Median Absolute Deviation (MAD)5950
Skewness0.60371608
Sum919464
Variance79418033
MonotonicityNot monotonic
2024-04-30T07:22:42.788451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12568 1
 
2.0%
24817 1
 
2.0%
24833 1
 
2.0%
17866 1
 
2.0%
10403 1
 
2.0%
13411 1
 
2.0%
14153 1
 
2.0%
30160 1
 
2.0%
14590 1
 
2.0%
16833 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
3131 1
2.0%
3394 1
2.0%
4578 1
2.0%
4680 1
2.0%
9307 1
2.0%
9593 1
2.0%
10403 1
2.0%
10414 1
2.0%
10849 1
2.0%
11426 1
2.0%
ValueCountFrequency (%)
45309 1
2.0%
34805 1
2.0%
32466 1
2.0%
31008 1
2.0%
30923 1
2.0%
30160 1
2.0%
28600 1
2.0%
27113 1
2.0%
26718 1
2.0%
26671 1
2.0%

인구수_남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9102.24
Minimum1654
Maximum22029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-04-30T07:22:42.911794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1654
5-th percentile2392.2
Q16161.75
median8009
Q312550.25
95-th percentile16138.05
Maximum22029
Range20375
Interquartile range (IQR)6388.5

Descriptive statistics

Standard deviation4290.1769
Coefficient of variation (CV)0.471332
Kurtosis0.4020462
Mean9102.24
Median Absolute Deviation (MAD)2496
Skewness0.61247511
Sum455112
Variance18405618
MonotonicityNot monotonic
2024-04-30T07:22:43.050824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6663 1
 
2.0%
12140 1
 
2.0%
12212 1
 
2.0%
8498 1
 
2.0%
5056 1
 
2.0%
6668 1
 
2.0%
7192 1
 
2.0%
14296 1
 
2.0%
6942 1
 
2.0%
8095 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
1654 1
2.0%
1796 1
2.0%
2367 1
2.0%
2423 1
2.0%
4616 1
2.0%
4724 1
2.0%
5056 1
2.0%
5821 1
2.0%
5845 1
2.0%
5850 1
2.0%
ValueCountFrequency (%)
22029 1
2.0%
17018 1
2.0%
17007 1
2.0%
15076 1
2.0%
15003 1
2.0%
14296 1
2.0%
13589 1
2.0%
13141 1
2.0%
13021 1
2.0%
12833 1
2.0%

인구수_여
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9287.04
Minimum1477
Maximum23280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-04-30T07:22:43.189952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1477
5-th percentile2226.1
Q15927.5
median8390.5
Q312663
95-th percentile15926.6
Maximum23280
Range21803
Interquartile range (IQR)6735.5

Descriptive statistics

Standard deviation4641.4295
Coefficient of variation (CV)0.49977491
Kurtosis0.26718096
Mean9287.04
Median Absolute Deviation (MAD)3114.5
Skewness0.60227362
Sum464352
Variance21542868
MonotonicityNot monotonic
2024-04-30T07:22:43.303229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5905 1
 
2.0%
12677 1
 
2.0%
12621 1
 
2.0%
9368 1
 
2.0%
5347 1
 
2.0%
6743 1
 
2.0%
6961 1
 
2.0%
15864 1
 
2.0%
7648 1
 
2.0%
8738 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
1477 1
2.0%
1598 1
2.0%
2155 1
2.0%
2313 1
2.0%
4569 1
2.0%
4691 1
2.0%
4869 1
2.0%
5028 1
2.0%
5347 1
2.0%
5382 1
2.0%
ValueCountFrequency (%)
23280 1
2.0%
17798 1
2.0%
15932 1
2.0%
15920 1
2.0%
15864 1
2.0%
15448 1
2.0%
15011 1
2.0%
14293 1
2.0%
13885 1
2.0%
13572 1
2.0%

19세 이상_계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15714.52
Minimum2843
Maximum36495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-04-30T07:22:43.421040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2843
5-th percentile3911.3
Q110687.25
median13935.5
Q321518.5
95-th percentile26914.6
Maximum36495
Range33652
Interquartile range (IQR)10831.25

Descriptive statistics

Standard deviation7329.1871
Coefficient of variation (CV)0.46639586
Kurtosis0.062239336
Mean15714.52
Median Absolute Deviation (MAD)4781
Skewness0.49774746
Sum785726
Variance53716983
MonotonicityNot monotonic
2024-04-30T07:22:43.536891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11729 1
 
2.0%
20338 1
 
2.0%
20815 1
 
2.0%
14713 1
 
2.0%
7551 1
 
2.0%
10512 1
 
2.0%
12159 1
 
2.0%
25476 1
 
2.0%
12842 1
 
2.0%
13781 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
2843 1
2.0%
3156 1
2.0%
3734 1
2.0%
4128 1
2.0%
7551 1
2.0%
8295 1
2.0%
8445 1
2.0%
9586 1
2.0%
9923 1
2.0%
10082 1
2.0%
ValueCountFrequency (%)
36495 1
2.0%
29885 1
2.0%
27613 1
2.0%
26061 1
2.0%
25476 1
2.0%
25277 1
2.0%
24853 1
2.0%
23561 1
2.0%
23456 1
2.0%
22110 1
2.0%

19세 이상_남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7737.36
Minimum1506
Maximum17634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-04-30T07:22:43.663886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1506
5-th percentile2037.25
Q15478.5
median6902.5
Q310318.75
95-th percentile12995.75
Maximum17634
Range16128
Interquartile range (IQR)4840.25

Descriptive statistics

Standard deviation3513.1076
Coefficient of variation (CV)0.45404474
Kurtosis0.26499366
Mean7737.36
Median Absolute Deviation (MAD)2157.5
Skewness0.5361142
Sum386868
Variance12341925
MonotonicityNot monotonic
2024-04-30T07:22:43.789955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6223 1
 
2.0%
9813 1
 
2.0%
10129 1
 
2.0%
6878 1
 
2.0%
3651 1
 
2.0%
5191 1
 
2.0%
6173 1
 
2.0%
11966 1
 
2.0%
6006 1
 
2.0%
6544 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
1506 1
2.0%
1672 1
2.0%
2008 1
2.0%
2073 1
2.0%
3651 1
2.0%
4107 1
2.0%
4121 1
2.0%
4842 1
2.0%
5153 1
2.0%
5191 1
2.0%
ValueCountFrequency (%)
17634 1
2.0%
15728 1
2.0%
13313 1
2.0%
12608 1
2.0%
12125 1
2.0%
11966 1
2.0%
11700 1
2.0%
11501 1
2.0%
11380 1
2.0%
10987 1
2.0%

19세 이상_여
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7977.16
Minimum1337
Maximum18861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-04-30T07:22:44.197189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1337
5-th percentile1874.05
Q15161.25
median7155.5
Q310645.75
95-th percentile13865.85
Maximum18861
Range17524
Interquartile range (IQR)5484.5

Descriptive statistics

Standard deviation3843.2315
Coefficient of variation (CV)0.48177942
Kurtosis-0.059268243
Mean7977.16
Median Absolute Deviation (MAD)2833.5
Skewness0.48243645
Sum398858
Variance14770429
MonotonicityNot monotonic
2024-04-30T07:22:44.328312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5506 1
 
2.0%
10525 1
 
2.0%
10686 1
 
2.0%
7835 1
 
2.0%
3900 1
 
2.0%
5321 1
 
2.0%
5986 1
 
2.0%
13510 1
 
2.0%
6836 1
 
2.0%
7237 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
1337 1
2.0%
1484 1
2.0%
1726 1
2.0%
2055 1
2.0%
3900 1
2.0%
4168 1
2.0%
4188 1
2.0%
4324 1
2.0%
4647 1
2.0%
5065 1
2.0%
ValueCountFrequency (%)
18861 1
2.0%
14300 1
2.0%
14157 1
2.0%
13510 1
2.0%
13453 1
2.0%
13153 1
2.0%
13152 1
2.0%
12574 1
2.0%
11955 1
2.0%
11683 1
2.0%

65세 이상_계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3106.5
Minimum570
Maximum6480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-04-30T07:22:44.453336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum570
5-th percentile849.3
Q12341
median2933.5
Q33936
95-th percentile5539.05
Maximum6480
Range5910
Interquartile range (IQR)1595

Descriptive statistics

Standard deviation1398.1122
Coefficient of variation (CV)0.45006025
Kurtosis-0.099137239
Mean3106.5
Median Absolute Deviation (MAD)750.5
Skewness0.33850873
Sum155325
Variance1954717.6
MonotonicityNot monotonic
2024-04-30T07:22:44.564213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2881 1
 
2.0%
3655 1
 
2.0%
3949 1
 
2.0%
2503 1
 
2.0%
1201 1
 
2.0%
1418 1
 
2.0%
2232 1
 
2.0%
4111 1
 
2.0%
2733 1
 
2.0%
2344 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
570 1
2.0%
652 1
2.0%
672 1
2.0%
1066 1
2.0%
1201 1
2.0%
1229 1
2.0%
1418 1
2.0%
1627 1
2.0%
1738 1
2.0%
2000 1
2.0%
ValueCountFrequency (%)
6480 1
2.0%
6165 1
2.0%
5670 1
2.0%
5379 1
2.0%
4935 1
2.0%
4902 1
2.0%
4897 1
2.0%
4849 1
2.0%
4481 1
2.0%
4186 1
2.0%

65세 이상_남자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1400.6
Minimum262
Maximum2999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-04-30T07:22:44.686467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum262
5-th percentile381.8
Q11053.5
median1314.5
Q31847
95-th percentile2407.25
Maximum2999
Range2737
Interquartile range (IQR)793.5

Descriptive statistics

Standard deviation635.23407
Coefficient of variation (CV)0.45354425
Kurtosis-0.012078009
Mean1400.6
Median Absolute Deviation (MAD)409
Skewness0.38120772
Sum70030
Variance403522.33
MonotonicityNot monotonic
2024-04-30T07:22:44.814094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1235 1
 
2.0%
1696 1
 
2.0%
1835 1
 
2.0%
1030 1
 
2.0%
532 1
 
2.0%
634 1
 
2.0%
1051 1
 
2.0%
1914 1
 
2.0%
1061 1
 
2.0%
1016 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
262 1
2.0%
315 1
2.0%
317 1
2.0%
461 1
2.0%
532 1
2.0%
594 1
2.0%
634 1
2.0%
726 1
2.0%
768 1
2.0%
878 1
2.0%
ValueCountFrequency (%)
2999 1
2.0%
2884 1
2.0%
2477 1
2.0%
2322 1
2.0%
2257 1
2.0%
2202 1
2.0%
2137 1
2.0%
2050 1
2.0%
2048 1
2.0%
1914 1
2.0%

65세 이상_여자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1705.9
Minimum308
Maximum3481
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-04-30T07:22:44.947660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum308
5-th percentile467.5
Q11280.75
median1655
Q32083.5
95-th percentile3131.8
Maximum3481
Range3173
Interquartile range (IQR)802.75

Descriptive statistics

Standard deviation768.164
Coefficient of variation (CV)0.45029838
Kurtosis-0.15044636
Mean1705.9
Median Absolute Deviation (MAD)422.5
Skewness0.31910987
Sum85295
Variance590075.93
MonotonicityNot monotonic
2024-04-30T07:22:45.078946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1646 1
 
2.0%
1959 1
 
2.0%
2114 1
 
2.0%
1473 1
 
2.0%
669 1
 
2.0%
784 1
 
2.0%
1181 1
 
2.0%
2197 1
 
2.0%
1672 1
 
2.0%
1328 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
308 1
2.0%
337 1
2.0%
355 1
2.0%
605 1
2.0%
635 1
2.0%
669 1
2.0%
784 1
2.0%
901 1
2.0%
970 1
2.0%
1122 1
2.0%
ValueCountFrequency (%)
3481 1
2.0%
3281 1
2.0%
3193 1
2.0%
3057 1
2.0%
2801 1
2.0%
2765 1
2.0%
2733 1
2.0%
2640 1
2.0%
2431 1
2.0%
2319 1
2.0%

세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8217.4
Minimum1645
Maximum18498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-04-30T07:22:45.208238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1645
5-th percentile2111.7
Q16380.75
median7650.5
Q310685.25
95-th percentile13354.55
Maximum18498
Range16853
Interquartile range (IQR)4304.5

Descriptive statistics

Standard deviation3743.4602
Coefficient of variation (CV)0.45555287
Kurtosis0.73375287
Mean8217.4
Median Absolute Deviation (MAD)1996
Skewness0.64699717
Sum410870
Variance14013494
MonotonicityNot monotonic
2024-04-30T07:22:45.331850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7678 1
 
2.0%
9022 1
 
2.0%
10116 1
 
2.0%
7808 1
 
2.0%
3189 1
 
2.0%
5159 1
 
2.0%
6365 1
 
2.0%
13922 1
 
2.0%
7333 1
 
2.0%
6638 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
1645 1
2.0%
2034 1
2.0%
2037 1
2.0%
2203 1
2.0%
3189 1
2.0%
4301 1
2.0%
4434 1
2.0%
4438 1
2.0%
5159 1
2.0%
5276 1
2.0%
ValueCountFrequency (%)
18498 1
2.0%
18448 1
2.0%
13922 1
2.0%
12661 1
2.0%
12616 1
2.0%
12611 1
2.0%
12596 1
2.0%
12218 1
2.0%
12180 1
2.0%
12132 1
2.0%

재외국민
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.1
Minimum5
Maximum286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-04-30T07:22:45.473467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8.45
Q118.5
median41
Q377.5
95-th percentile161.55
Maximum286
Range281
Interquartile range (IQR)59

Descriptive statistics

Standard deviation54.820021
Coefficient of variation (CV)0.96007042
Kurtosis5.3030079
Mean57.1
Median Absolute Deviation (MAD)24.5
Skewness2.0212777
Sum2855
Variance3005.2347
MonotonicityNot monotonic
2024-04-30T07:22:45.595895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
23 3
 
6.0%
27 3
 
6.0%
16 2
 
4.0%
85 2
 
4.0%
58 2
 
4.0%
41 2
 
4.0%
20 2
 
4.0%
17 2
 
4.0%
161 1
 
2.0%
152 1
 
2.0%
Other values (30) 30
60.0%
ValueCountFrequency (%)
5 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
11 1
2.0%
12 1
2.0%
13 1
2.0%
16 2
4.0%
17 2
4.0%
ValueCountFrequency (%)
286 1
2.0%
168 1
2.0%
162 1
2.0%
161 1
2.0%
152 1
2.0%
130 1
2.0%
116 1
2.0%
90 1
2.0%
88 1
2.0%
85 2
4.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
Minimum2024-03-31 00:00:00
Maximum2024-03-31 00:00:00
2024-04-30T07:22:45.689062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:45.763967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-30T07:22:40.745123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:31.247578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:32.252930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:33.237400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:34.114200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:34.996060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:36.137269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:36.994934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:37.908681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:38.756536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:39.624617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:40.819059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:31.382946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:32.352590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:33.310413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:34.182787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:35.074235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:36.204815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:37.071298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:37.974383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:38.828789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:39.691361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:40.905736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:31.496567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:32.462559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:33.404233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:34.266444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:35.159660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:36.288826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:37.167279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:38.058853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:38.911997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:40.023540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:40.974328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:31.568834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:32.554544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:33.476960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:34.330560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:35.233395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:36.364886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:37.233203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:38.124494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:38.979164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:40.094409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:41.048502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:31.635904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:32.641792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:33.580122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:34.405777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:35.523651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:36.444162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:37.316674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:38.210470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:39.056965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:40.182362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:41.134249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:31.710260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:32.730918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:33.661026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:34.495609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:35.603231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:36.535882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:37.406534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:38.307303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:39.148072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:40.260628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:41.217259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:31.780784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:32.818454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:33.732625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:34.588093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:35.698090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:36.610497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:37.503810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:38.380701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:39.227263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:40.341053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:41.293564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:31.854330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:32.904891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:33.806119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:34.671210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:35.785995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:36.683376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:37.583276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:38.456971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:39.294907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:40.416896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:41.371142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:31.929468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:32.980950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:33.880554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:34.759145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:35.877947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:36.754146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:37.655649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:38.525477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:39.371115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:40.491683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:41.448292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:32.028189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:33.071011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:33.955242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:34.837165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:35.963408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:36.833843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:37.735602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:38.601656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:39.454230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:40.578709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:41.525872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:32.143670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:33.149793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:34.036546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:34.914349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:36.058005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:36.907284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:37.823932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:38.676501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:39.537442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:22:40.666211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:22:45.839038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구별인구수_계인구수_남인구수_여19세 이상_계19세 이상_남19세 이상_여65세 이상_계65세 이상_남자65세 이상_여자세대수재외국민
구별1.0001.0000.6650.7180.7320.5850.4890.7200.2460.0000.0000.0000.647
1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
인구수_계0.6651.0001.0000.9910.9990.9420.9380.9870.7740.7150.7560.8940.651
인구수_남0.7181.0000.9911.0000.9850.9570.9590.9920.8060.7160.7840.9260.610
인구수_여0.7321.0000.9990.9851.0000.9350.9330.9810.7700.6950.7710.8830.649
19세 이상_계0.5851.0000.9420.9570.9351.0000.9990.9710.9180.8820.8800.8610.625
19세 이상_남0.4891.0000.9380.9590.9330.9991.0000.9540.8990.8700.8710.8660.543
19세 이상_여0.7201.0000.9870.9920.9810.9710.9541.0000.8190.7450.7610.9260.605
65세 이상_계0.2461.0000.7740.8060.7700.9180.8990.8191.0000.9680.9950.8020.469
65세 이상_남자0.0001.0000.7150.7160.6950.8820.8700.7450.9681.0000.9450.8110.306
65세 이상_여자0.0001.0000.7560.7840.7710.8800.8710.7610.9950.9451.0000.8080.305
세대수0.0001.0000.8940.9260.8830.8610.8660.9260.8020.8110.8081.0000.726
재외국민0.6471.0000.6510.6100.6490.6250.5430.6050.4690.3060.3050.7261.000
2024-04-30T07:22:45.974913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인구수_계인구수_남인구수_여19세 이상_계19세 이상_남19세 이상_여65세 이상_계65세 이상_남자65세 이상_여자세대수재외국민구별
인구수_계1.0000.9950.9960.9900.9750.9950.8500.8550.8430.9230.7800.315
인구수_남0.9951.0000.9880.9920.9840.9890.8610.8710.8470.9320.7570.359
인구수_여0.9960.9881.0000.9840.9640.9940.8380.8410.8320.9060.7970.373
19세 이상_계0.9900.9920.9841.0000.9920.9930.8940.8980.8830.9530.7380.350
19세 이상_남0.9750.9840.9640.9921.0000.9750.9110.9160.8950.9670.6830.257
19세 이상_여0.9950.9890.9940.9930.9751.0000.8690.8710.8630.9270.7760.360
65세 이상_계0.8500.8610.8380.8940.9110.8691.0000.9910.9920.9100.5170.173
65세 이상_남자0.8550.8710.8410.8980.9160.8710.9911.0000.9720.9020.5200.000
65세 이상_여자0.8430.8470.8320.8830.8950.8630.9920.9721.0000.9030.5250.000
세대수0.9230.9320.9060.9530.9670.9270.9100.9020.9031.0000.6030.000
재외국민0.7800.7570.7970.7380.6830.7760.5170.5200.5250.6031.0000.523
구별0.3150.3590.3730.3500.2570.3600.1730.0000.0000.0000.5231.000

Missing values

2024-04-30T07:22:41.640965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:22:41.821593image/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

구별인구수_계인구수_남인구수_여19세 이상_계19세 이상_남19세 이상_여65세 이상_계65세 이상_남자65세 이상_여자세대수재외국민데이터기준일자
0수정구신흥1동125686663590511729622355062881123516467678162024-03-31
1수정구신흥2동31008150761593226061126081345344812050243112132452024-03-31
2수정구신흥3동10849582150281008254354647234010751265644292024-03-31
3수정구태평1동142887403688513398694864503360150018608505232024-03-31
4수정구태평2동143087205710313043656864753122133617867753182024-03-31
5수정구태평3동123696374599511314585354612815124915666669172024-03-31
6수정구태평4동116586008565010501539351082463107313906188132024-03-31
7수정구수진1동114266044538210754568950652973131916546902122024-03-31
8수정구수진2동149237581734213632692767053606164319637623322024-03-31
9수정구단대동145307159737112793624865452964126417006522272024-03-31
구별인구수_계인구수_남인구수_여19세 이상_계19세 이상_남19세 이상_여65세 이상_계65세 이상_남자65세 이상_여자세대수재외국민데이터기준일자
40분당구야탑1동167978293850414668720274663101144116607569602024-03-31
41분당구야탑2동157887450833813299622570743293151817756033792024-03-31
42분당구야탑3동26671131411353023456115011195549022137276512616622024-03-31
43분당구금곡동271131282014293235611098712574537923223057120551522024-03-31
44분당구구미동286001358915011248531170013153567024773193126111612024-03-31
45분당구구미1동163667923844314090678173092578118013987080852024-03-31
46분당구판교동265141302113493202889802104862780133414469010902024-03-31
47분당구삼평동23891115651232619148915799912903131015939064582024-03-31
48분당구백현동26718128331388522110104271168335841612197210464852024-03-31
49분당구운중동348051700717798276131331314300389719051992126611622024-03-31