Overview

Dataset statistics

Number of variables15
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory136.6 B

Variable types

DateTime1
Categorical2
Text1
Numeric11

Dataset

Description샘플 데이터
Author경기도경제과학진흥원
URLhttps://www.bigdata-region.kr/#/dataset/9d8c20d9-a9ca-4687-b3de-ade09d5664c1

Alerts

년월 has constant value ""Constant
시도명 has constant value ""Constant
총거주인구수 is highly overall correlated with 0~9세 and 9 other fieldsHigh correlation
0~9세 is highly overall correlated with 총거주인구수 and 9 other fieldsHigh correlation
10~19세 is highly overall correlated with 총거주인구수 and 9 other fieldsHigh correlation
20~29세 is highly overall correlated with 총거주인구수 and 9 other fieldsHigh correlation
30~39세 is highly overall correlated with 총거주인구수 and 9 other fieldsHigh correlation
40~49세 is highly overall correlated with 총거주인구수 and 9 other fieldsHigh correlation
50~59세 is highly overall correlated with 총거주인구수 and 9 other fieldsHigh correlation
60~69세 is highly overall correlated with 총거주인구수 and 9 other fieldsHigh correlation
70~79세 is highly overall correlated with 총거주인구수 and 9 other fieldsHigh correlation
80~89세 is highly overall correlated with 총거주인구수 and 9 other fieldsHigh correlation
90~99세 is highly overall correlated with 총거주인구수 and 9 other fieldsHigh correlation
총결제금액 is highly imbalanced (78.4%)Imbalance
시군구명 has unique valuesUnique
총거주인구수 has unique valuesUnique
0~9세 has unique valuesUnique
20~29세 has unique valuesUnique
30~39세 has unique valuesUnique
40~49세 has unique valuesUnique
50~59세 has unique valuesUnique
60~69세 has unique valuesUnique
70~79세 has unique valuesUnique
80~89세 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:59:04.754213
Analysis finished2023-12-10 13:59:26.798608
Duration22.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년월
Date

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
Minimum2019-03-01 00:00:00
Maximum2019-03-01 00:00:00
2023-12-10T22:59:26.868309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:27.022010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
경기도
29 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 29
100.0%

Length

2023-12-10T22:59:27.173512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:59:27.320989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 29
100.0%

시군구명
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-10T22:59:27.544328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.0689655
Min length3

Characters and Unicode

Total characters147
Distinct characters45
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row가평군
2nd row고양시 덕양구
3rd row고양시 일산동구
4th row고양시 일산서구
5th row과천시
ValueCountFrequency (%)
수원시 4
 
9.3%
고양시 3
 
7.0%
성남시 3
 
7.0%
안산시 2
 
4.7%
안양시 2
 
4.7%
권선구 1
 
2.3%
영통구 1
 
2.3%
장안구 1
 
2.3%
팔달구 1
 
2.3%
시흥시 1
 
2.3%
Other values (24) 24
55.8%
2023-12-10T22:59:28.103506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 28
19.0%
구 15
 
10.2%
14
 
9.5%
양 9
 
6.1%
안 8
 
5.4%
원 6
 
4.1%
수 5
 
3.4%
주 4
 
2.7%
산 4
 
2.7%
성 4
 
2.7%
Other values (35) 50
34.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 133
90.5%
Space Separator 14
 
9.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 28
21.1%
구 15
 
11.3%
양 9
 
6.8%
안 8
 
6.0%
원 6
 
4.5%
수 5
 
3.8%
주 4
 
3.0%
산 4
 
3.0%
성 4
 
3.0%
남 4
 
3.0%
Other values (34) 46
34.6%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 133
90.5%
Common 14
 
9.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 28
21.1%
구 15
 
11.3%
양 9
 
6.8%
안 8
 
6.0%
원 6
 
4.5%
수 5
 
3.8%
주 4
 
3.0%
산 4
 
3.0%
성 4
 
3.0%
남 4
 
3.0%
Other values (34) 46
34.6%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 133
90.5%
ASCII 14
 
9.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 28
21.1%
구 15
 
11.3%
양 9
 
6.8%
안 8
 
6.0%
원 6
 
4.5%
수 5
 
3.8%
주 4
 
3.0%
산 4
 
3.0%
성 4
 
3.0%
남 4
 
3.0%
Other values (34) 46
34.6%
ASCII
ValueCountFrequency (%)
14
100.0%

총거주인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean306127.66
Minimum58289
Maximum829996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-10T22:59:28.368146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum58289
5-th percentile75356.2
Q1199265
median297671
Q3371754
95-th percentile616300
Maximum829996
Range771707
Interquartile range (IQR)172489

Descriptive statistics

Standard deviation174989.53
Coefficient of variation (CV)0.57162273
Kurtosis2.103528
Mean306127.66
Median Absolute Deviation (MAD)77630
Skewness1.1682133
Sum8877702
Variance3.0621335 × 1010
MonotonicityNot monotonic
2023-12-10T22:59:28.716088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
62415 1
 
3.4%
466157 1
 
3.4%
111083 1
 
3.4%
116874 1
 
3.4%
222314 1
 
3.4%
244235 1
 
3.4%
322809 1
 
3.4%
183405 1
 
3.4%
344613 1
 
3.4%
306305 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
58289 1
3.4%
62415 1
3.4%
94768 1
3.4%
111083 1
3.4%
116874 1
3.4%
175866 1
3.4%
183405 1
3.4%
199265 1
3.4%
220041 1
3.4%
222314 1
3.4%
ValueCountFrequency (%)
829996 1
3.4%
701830 1
3.4%
488005 1
3.4%
473682 1
3.4%
466157 1
3.4%
437221 1
3.4%
372654 1
3.4%
371754 1
3.4%
368468 1
3.4%
344613 1
3.4%

0~9세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25682.586
Minimum3607
Maximum67577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-10T22:59:28.978975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3607
5-th percentile5473
Q114940
median22496
Q336223
95-th percentile58027.2
Maximum67577
Range63970
Interquartile range (IQR)21283

Descriptive statistics

Standard deviation16685.873
Coefficient of variation (CV)0.649696
Kurtosis0.37961277
Mean25682.586
Median Absolute Deviation (MAD)12039
Skewness0.92646722
Sum744795
Variance2.7841837 × 108
MonotonicityNot monotonic
2023-12-10T22:59:29.214633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3607 1
 
3.4%
38863 1
 
3.4%
7716 1
 
3.4%
7620 1
 
3.4%
19814 1
 
3.4%
16841 1
 
3.4%
25926 1
 
3.4%
14940 1
 
3.4%
23943 1
 
3.4%
22496 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
3607 1
3.4%
4507 1
3.4%
6922 1
3.4%
7620 1
3.4%
7716 1
3.4%
10457 1
3.4%
14246 1
3.4%
14940 1
3.4%
16074 1
3.4%
16345 1
3.4%
ValueCountFrequency (%)
67577 1
3.4%
61998 1
3.4%
52071 1
3.4%
46800 1
3.4%
41685 1
3.4%
39097 1
3.4%
38863 1
3.4%
36223 1
3.4%
34659 1
3.4%
25926 1
3.4%

10~19세
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30729.31
Minimum4857
Maximum77825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-10T22:59:29.389935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4857
5-th percentile7421.6
Q117859
median30872
Q335705
95-th percentile66962.6
Maximum77825
Range72968
Interquartile range (IQR)17846

Descriptive statistics

Standard deviation18310.073
Coefficient of variation (CV)0.59585045
Kurtosis0.90089709
Mean30729.31
Median Absolute Deviation (MAD)12607
Skewness0.9034103
Sum891150
Variance3.3525879 × 108
MonotonicityNot monotonic
2023-12-10T22:59:29.726559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
35705 2
 
6.9%
4857 1
 
3.4%
17829 1
 
3.4%
9952 1
 
3.4%
9758 1
 
3.4%
23932 1
 
3.4%
20067 1
 
3.4%
18265 1
 
3.4%
32427 1
 
3.4%
49958 1
 
3.4%
Other values (18) 18
62.1%
ValueCountFrequency (%)
4857 1
3.4%
6366 1
3.4%
9005 1
3.4%
9758 1
3.4%
9952 1
3.4%
14791 1
3.4%
17829 1
3.4%
17859 1
3.4%
18265 1
3.4%
19116 1
3.4%
ValueCountFrequency (%)
77825 1
3.4%
74557 1
3.4%
55571 1
3.4%
49958 1
3.4%
44797 1
3.4%
43693 1
3.4%
42557 1
3.4%
35705 2
6.9%
35571 1
3.4%
34800 1
3.4%

20~29세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41975.586
Minimum6174
Maximum119747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-10T22:59:29.984774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6174
5-th percentile8996.2
Q127505
median42890
Q355216
95-th percentile76429.6
Maximum119747
Range113573
Interquartile range (IQR)27711

Descriptive statistics

Standard deviation24401.635
Coefficient of variation (CV)0.58132924
Kurtosis2.4409083
Mean41975.586
Median Absolute Deviation (MAD)14719
Skewness1.0413345
Sum1217292
Variance5.9543981 × 108
MonotonicityNot monotonic
2023-12-10T22:59:30.322718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
6174 1
 
3.4%
62288 1
 
3.4%
12067 1
 
3.4%
10456 1
 
3.4%
26280 1
 
3.4%
35566 1
 
3.4%
45794 1
 
3.4%
21456 1
 
3.4%
56696 1
 
3.4%
48423 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
6174 1
3.4%
8023 1
3.4%
10456 1
3.4%
11201 1
3.4%
12067 1
3.4%
21456 1
3.4%
26280 1
3.4%
27505 1
3.4%
28171 1
3.4%
31528 1
3.4%
ValueCountFrequency (%)
119747 1
3.4%
82728 1
3.4%
66982 1
3.4%
66027 1
3.4%
62288 1
3.4%
58836 1
3.4%
56696 1
3.4%
55216 1
3.4%
48423 1
3.4%
47422 1
3.4%

30~39세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43540.793
Minimum5992
Maximum121226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-10T22:59:30.550122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5992
5-th percentile8936.8
Q127578
median38563
Q361001
95-th percentile84883.2
Maximum121226
Range115234
Interquartile range (IQR)33423

Descriptive statistics

Standard deviation26690.291
Coefficient of variation (CV)0.61299505
Kurtosis1.1926832
Mean43540.793
Median Absolute Deviation (MAD)14931
Skewness0.93242341
Sum1262683
Variance7.1237162 × 108
MonotonicityNot monotonic
2023-12-10T22:59:30.771703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5992 1
 
3.4%
66204 1
 
3.4%
12009 1
 
3.4%
11218 1
 
3.4%
27614 1
 
3.4%
34566 1
 
3.4%
46096 1
 
3.4%
23632 1
 
3.4%
43859 1
 
3.4%
42003 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
5992 1
3.4%
7806 1
3.4%
10633 1
3.4%
11218 1
3.4%
12009 1
3.4%
23632 1
3.4%
25322 1
3.4%
27578 1
3.4%
27614 1
3.4%
32114 1
3.4%
ValueCountFrequency (%)
121226 1
3.4%
92256 1
3.4%
73824 1
3.4%
72353 1
3.4%
71134 1
3.4%
66204 1
3.4%
64472 1
3.4%
61001 1
3.4%
59830 1
3.4%
46096 1
3.4%

40~49세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52268.966
Minimum8097
Maximum132971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-10T22:59:30.959746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8097
5-th percentile11501
Q133603
median52585
Q364543
95-th percentile112760
Maximum132971
Range124874
Interquartile range (IQR)30940

Descriptive statistics

Standard deviation31150.767
Coefficient of variation (CV)0.5959706
Kurtosis1.0982386
Mean52268.966
Median Absolute Deviation (MAD)18174
Skewness0.95306784
Sum1515800
Variance9.7037027 × 108
MonotonicityNot monotonic
2023-12-10T22:59:31.339080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
8097 1
 
3.4%
78358 1
 
3.4%
16168 1
 
3.4%
16328 1
 
3.4%
38583 1
 
3.4%
36457 1
 
3.4%
55506 1
 
3.4%
29912 1
 
3.4%
58702 1
 
3.4%
52585 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
8097 1
3.4%
9209 1
3.4%
14939 1
3.4%
16168 1
3.4%
16328 1
3.4%
28252 1
3.4%
29912 1
3.4%
33603 1
3.4%
34411 1
3.4%
36457 1
3.4%
ValueCountFrequency (%)
132971 1
3.4%
128868 1
3.4%
88598 1
3.4%
86398 1
3.4%
80358 1
3.4%
78358 1
3.4%
68605 1
3.4%
64543 1
3.4%
61712 1
3.4%
58702 1
3.4%

50~59세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52648.31
Minimum10278
Maximum150997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-10T22:59:31.593826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10278
5-th percentile13527.6
Q135905
median52573
Q362658
95-th percentile101469
Maximum150997
Range140719
Interquartile range (IQR)26753

Descriptive statistics

Standard deviation29869.998
Coefficient of variation (CV)0.56734961
Kurtosis3.3182122
Mean52648.31
Median Absolute Deviation (MAD)14366
Skewness1.3777354
Sum1526801
Variance8.922168 × 108
MonotonicityNot monotonic
2023-12-10T22:59:31.786198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
11668 1
 
3.4%
81204 1
 
3.4%
20203 1
 
3.4%
21018 1
 
3.4%
36809 1
 
3.4%
44938 1
 
3.4%
56290 1
 
3.4%
30832 1
 
3.4%
66939 1
 
3.4%
59200 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
10278 1
3.4%
11668 1
3.4%
16317 1
3.4%
20203 1
3.4%
21018 1
3.4%
30823 1
3.4%
30832 1
3.4%
35905 1
3.4%
36809 1
3.4%
39834 1
3.4%
ValueCountFrequency (%)
150997 1
3.4%
113759 1
3.4%
83034 1
3.4%
81204 1
3.4%
76120 1
3.4%
66939 1
3.4%
62744 1
3.4%
62658 1
3.4%
61520 1
3.4%
59200 1
3.4%

60~69세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33436.552
Minimum6498
Maximum102122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-10T22:59:32.093525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6498
5-th percentile11663.4
Q123032
median30187
Q337128
95-th percentile66351.2
Maximum102122
Range95624
Interquartile range (IQR)14096

Descriptive statistics

Standard deviation18957.708
Coefficient of variation (CV)0.56697558
Kurtosis5.8785886
Mean33436.552
Median Absolute Deviation (MAD)7155
Skewness2.0355421
Sum969660
Variance3.5939471 × 108
MonotonicityNot monotonic
2023-12-10T22:59:32.297485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
11209 1
 
3.4%
51362 1
 
3.4%
17022 1
 
3.4%
20964 1
 
3.4%
26142 1
 
3.4%
32324 1
 
3.4%
34410 1
 
3.4%
23032 1
 
3.4%
33507 1
 
3.4%
30187 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
6498 1
3.4%
11209 1
3.4%
12345 1
3.4%
17022 1
3.4%
20964 1
3.4%
21123 1
3.4%
22583 1
3.4%
23032 1
3.4%
24268 1
3.4%
26142 1
3.4%
ValueCountFrequency (%)
102122 1
3.4%
76344 1
3.4%
51362 1
3.4%
46825 1
3.4%
43857 1
3.4%
43558 1
3.4%
40269 1
3.4%
37128 1
3.4%
36524 1
3.4%
34410 1
3.4%

70~79세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17071.724
Minimum3457
Maximum44498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-10T22:59:32.476831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3457
5-th percentile7565.4
Q112295
median15299
Q318027
95-th percentile37500.8
Maximum44498
Range41041
Interquartile range (IQR)5732

Descriptive statistics

Standard deviation9056.3416
Coefficient of variation (CV)0.53048781
Kurtosis3.8602193
Mean17071.724
Median Absolute Deviation (MAD)2879
Skewness1.8152336
Sum495080
Variance82017323
MonotonicityNot monotonic
2023-12-10T22:59:32.705168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
6793 1
 
3.4%
29459 1
 
3.4%
10072 1
 
3.4%
12635 1
 
3.4%
15620 1
 
3.4%
15850 1
 
3.4%
14896 1
 
3.4%
13210 1
 
3.4%
16224 1
 
3.4%
12295 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
3457 1
3.4%
6793 1
3.4%
8724 1
3.4%
10072 1
3.4%
10870 1
3.4%
11399 1
3.4%
11570 1
3.4%
12295 1
3.4%
12635 1
3.4%
13210 1
3.4%
ValueCountFrequency (%)
44498 1
3.4%
42862 1
3.4%
29459 1
3.4%
25176 1
3.4%
23886 1
3.4%
20728 1
3.4%
18178 1
3.4%
18027 1
3.4%
16855 1
3.4%
16367 1
3.4%

80~89세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7565.2069
Minimum1740
Maximum18755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-10T22:59:32.916076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1740
5-th percentile3702.2
Q15345
median7098
Q38101
95-th percentile15712
Maximum18755
Range17015
Interquartile range (IQR)2756

Descriptive statistics

Standard deviation3750.454
Coefficient of variation (CV)0.49575035
Kurtosis2.8183208
Mean7565.2069
Median Absolute Deviation (MAD)1283
Skewness1.5818832
Sum219391
Variance14065905
MonotonicityNot monotonic
2023-12-10T22:59:33.137294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3479 1
 
3.4%
13765 1
 
3.4%
5090 1
 
3.4%
5916 1
 
3.4%
6530 1
 
3.4%
6641 1
 
3.4%
7033 1
 
3.4%
7164 1
 
3.4%
7849 1
 
3.4%
5815 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
1740 1
3.4%
3479 1
3.4%
4037 1
3.4%
4555 1
3.4%
4581 1
3.4%
4762 1
3.4%
5090 1
3.4%
5345 1
3.4%
5815 1
3.4%
5843 1
3.4%
ValueCountFrequency (%)
18755 1
3.4%
17010 1
3.4%
13765 1
3.4%
12402 1
3.4%
9519 1
3.4%
8720 1
3.4%
8245 1
3.4%
8101 1
3.4%
7849 1
3.4%
7573 1
3.4%

90~99세
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1117.1724
Minimum356
Maximum2899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-10T22:59:33.347951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum356
5-th percentile522.4
Q1822
median1066
Q31155
95-th percentile2322.8
Maximum2899
Range2543
Interquartile range (IQR)333

Descriptive statistics

Standard deviation570.69889
Coefficient of variation (CV)0.51084227
Kurtosis3.0428734
Mean1117.1724
Median Absolute Deviation (MAD)202
Skewness1.7115461
Sum32398
Variance325697.22
MonotonicityNot monotonic
2023-12-10T22:59:33.547672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1136 2
 
6.9%
1066 2
 
6.9%
494 1
 
3.4%
1900 1
 
3.4%
746 1
 
3.4%
901 1
 
3.4%
926 1
 
3.4%
905 1
 
3.4%
912 1
 
3.4%
822 1
 
3.4%
Other values (17) 17
58.6%
ValueCountFrequency (%)
356 1
3.4%
494 1
3.4%
565 1
3.4%
604 1
3.4%
689 1
3.4%
736 1
3.4%
746 1
3.4%
822 1
3.4%
838 1
3.4%
864 1
3.4%
ValueCountFrequency (%)
2899 1
3.4%
2392 1
3.4%
2219 1
3.4%
1900 1
3.4%
1367 1
3.4%
1247 1
3.4%
1160 1
3.4%
1155 1
3.4%
1136 2
6.9%
1123 1
3.4%

총결제금액
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
28 
33800
 
1

Length

Max length5
Median length1
Mean length1.137931
Min length1

Unique

Unique1 ?
Unique (%)3.4%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28
96.6%
33800 1
 
3.4%

Length

2023-12-10T22:59:33.784207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:59:34.135360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
96.6%
33800 1
 
3.4%

Interactions

2023-12-10T22:59:24.222243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:05.701318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:07.398890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:09.503857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:10.848144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:12.610638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:15.014843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:16.958428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:18.752314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:20.801804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:22.270846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:24.424103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:05.939664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:07.578357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:09.646420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:10.988592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:12.931403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:15.147563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:17.120985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:18.914393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:20.940367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:22.454730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:24.567085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:06.085401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:07.727754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:09.781059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:11.118527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:13.103070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:15.289921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:17.266459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:19.045160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:21.081703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:22.636793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:24.706226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:06.260716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:07.876715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:09.920757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:11.240993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:13.304132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:15.457264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:17.411864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:19.386067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:21.227064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:22.871061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:24.898382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:06.411950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:08.160811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:10.053716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:11.356873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:13.804875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:15.611081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:17.560005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:19.579495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:21.353734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:23.021482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:25.416878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:06.542392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:08.470054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:10.182740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:11.463289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:14.035178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:15.753814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:17.772292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:19.840642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:21.483552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:23.150532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:25.558091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:06.679651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:08.673040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:10.312644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:11.607764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:14.227323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:15.938303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:17.928799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:20.019038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:21.611272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:23.338001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:25.810509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:06.816638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:08.893490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:10.421257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:11.774963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:14.499405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:16.248790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:18.077852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:20.249458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:21.719361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:23.531471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:25.941279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:06.965975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:09.069271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:10.529661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:11.969649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:14.632806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:16.506607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:18.269348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:20.380126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:21.836173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:23.702127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:26.069608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:07.106263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:09.208231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:10.632747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:12.285819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:14.754434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:16.681057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:18.409551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:20.523077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:21.968041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:23.856661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:26.203239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:07.270808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:09.363311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:10.744659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:12.458327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:14.894208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:16.819074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:18.557364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:20.678785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:22.136060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:24.064045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:59:34.252755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명총거주인구수0~9세10~19세20~29세30~39세40~49세50~59세60~69세70~79세80~89세90~99세총결제금액
시군구명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
총거주인구수1.0001.0000.8770.9520.9860.9900.9700.9910.9170.8590.9270.9000.000
0~9세1.0000.8771.0000.9190.8140.8580.8830.7830.7450.7200.5970.3710.000
10~19세1.0000.9520.9191.0000.9610.9330.9820.9180.6600.6900.8570.7890.725
20~29세1.0000.9860.8140.9611.0000.9810.9740.9890.8370.6900.9230.9060.000
30~39세1.0000.9900.8580.9330.9811.0000.9530.9820.8780.6150.8770.8420.000
40~49세1.0000.9700.8830.9820.9740.9531.0000.9380.7270.7700.8820.7690.000
50~59세1.0000.9910.7830.9180.9890.9820.9381.0000.8680.8440.9350.9260.000
60~69세1.0000.9170.7450.6600.8370.8780.7270.8681.0000.8600.8850.8880.000
70~79세1.0000.8590.7200.6900.6900.6150.7700.8440.8601.0000.9620.9530.000
80~89세1.0000.9270.5970.8570.9230.8770.8820.9350.8850.9621.0000.9860.000
90~99세1.0000.9000.3710.7890.9060.8420.7690.9260.8880.9530.9861.0000.000
총결제금액1.0000.0000.0000.7250.0000.0000.0000.0000.0000.0000.0000.0001.000
2023-12-10T22:59:34.558119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총거주인구수0~9세10~19세20~29세30~39세40~49세50~59세60~69세70~79세80~89세90~99세총결제금액
총거주인구수1.0000.9800.9740.9600.9870.9940.9800.9230.8470.8350.8260.000
0~9세0.9801.0000.9820.9260.9800.9870.9420.8790.8000.8100.8040.000
10~19세0.9740.9821.0000.9510.9620.9830.9550.8510.7760.8210.8170.491
20~29세0.9600.9260.9511.0000.9530.9540.9640.8460.7370.7620.7650.000
30~39세0.9870.9800.9620.9531.0000.9840.9590.9190.8150.7890.7800.000
40~49세0.9940.9870.9830.9540.9841.0000.9670.8980.8330.8340.8310.000
50~59세0.9800.9420.9550.9640.9590.9671.0000.9170.8410.8580.8480.000
60~69세0.9230.8790.8510.8460.9190.8980.9171.0000.9370.8460.8320.000
70~79세0.8470.8000.7760.7370.8150.8330.8410.9371.0000.8960.8780.000
80~89세0.8350.8100.8210.7620.7890.8340.8580.8460.8961.0000.9830.000
90~99세0.8260.8040.8170.7650.7800.8310.8480.8320.8780.9831.0000.000
총결제금액0.0000.0000.4910.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T22:59:26.389231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:59:26.668162image/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

년월시도명시군구명총거주인구수0~9세10~19세20~29세30~39세40~49세50~59세60~69세70~79세80~89세90~99세총결제금액
02019-03경기도가평군62415360748576174599280971166811209679334794940
12019-03경기도고양시 덕양구46615738863425576228866204783588120451362294591376519000
22019-03경기도고양시 일산동구2976712185730872445023856352855548132948315174824512470
32019-03경기도고양시 일산서구3025232409335571428903685054307551062813815636872011600
42019-03경기도과천시5828945076366802378069209102786498345717403560
52019-03경기도광명시3165522572433063409504522755010525733712818178748510740
62019-03경기도광주시3726543622333577438855983064543615204355820728757311230
72019-03경기도구리시199265163451911627505275783360335905225831139945556040
82019-03경기도군포시2758522260228069382143987745760483163021214536709811000
92019-03경기도김포시4372215207144797474227113480358627444385723886951913670
년월시도명시군구명총거주인구수0~9세10~19세20~29세30~39세40~49세50~59세60~69세70~79세80~89세90~99세총결제금액
192019-03경기도수원시 팔달구175866104571479128171253222825230823211231157045816890
202019-03경기도시흥시4736824680049958660277382486398830344026918027810111550
212019-03경기도안산시 단원구306305224963242748423420035258559200301871229558158220
222019-03경기도안산시 상록구3446132394335705566964385958702669393350716224784911360
232019-03경기도안성시183405149401826521456236322991230832230321321071649120
242019-03경기도안양시 동안구3228092592635705457944609655506562903441014896703310660
252019-03경기도안양시 만안구244235168412006735566345663645744938323241585066419050
262019-03경기도양주시2223141981423932262802761438583368092614215620653092633800
272019-03경기도양평군1168747620975810456112181632821018209641263559169010
282019-03경기도여주시1110837716995212067120091616820203170221007250907460