Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells50000
Missing cells (%)29.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory159.0 B

Variable types

Numeric9
Categorical2
Text1
Unsupported5

Dataset

Description경기도_주민등록인구통계주민등록인구및세대현황정제기본
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=NIP3UX6DWQ56FJCD9BKG34692244&infSeq=1

Alerts

기준연월 is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
시군코드 is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
행정동코드 is highly overall correlated with 기준연월 and 2 other fieldsHigh correlation
총인구수 is highly overall correlated with 세대수 and 3 other fieldsHigh correlation
세대수 is highly overall correlated with 총인구수 and 2 other fieldsHigh correlation
세대당인구수 is highly overall correlated with 총인구수 and 3 other fieldsHigh correlation
남성인구수 is highly overall correlated with 총인구수 and 3 other fieldsHigh correlation
여성인구수 is highly overall correlated with 총인구수 and 3 other fieldsHigh correlation
남성여성비율 is highly overall correlated with 세대당인구수High correlation
시군명 is highly overall correlated with 시군코드High correlation
생성일자 is highly overall correlated with 기준연월 and 1 other fieldsHigh correlation
동단위분석시작연월 has 10000 (100.0%) missing valuesMissing
동단위분석종료연월 has 10000 (100.0%) missing valuesMissing
동단위분석시작년도 has 10000 (100.0%) missing valuesMissing
동단위분석종료년도 has 10000 (100.0%) missing valuesMissing
마트완료여부 has 10000 (100.0%) missing valuesMissing
동단위분석시작연월 is an unsupported type, check if it needs cleaning or further analysisUnsupported
동단위분석종료연월 is an unsupported type, check if it needs cleaning or further analysisUnsupported
동단위분석시작년도 is an unsupported type, check if it needs cleaning or further analysisUnsupported
동단위분석종료년도 is an unsupported type, check if it needs cleaning or further analysisUnsupported
마트완료여부 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-12 23:31:56.172400
Analysis finished2024-03-12 23:32:05.105531
Duration8.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연월
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201700.48
Minimum201601
Maximum201810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:32:05.162672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201601
5-th percentile201602
Q1201609
median201706
Q3201802
95-th percentile201809
Maximum201810
Range209
Interquartile range (IQR)193

Descriptive statistics

Standard deviation80.224081
Coefficient of variation (CV)0.00039773867
Kurtosis-1.4500324
Mean201700.48
Median Absolute Deviation (MAD)97
Skewness0.096545666
Sum2.0170048 × 109
Variance6435.9031
MonotonicityNot monotonic
2024-03-13T08:32:05.261049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
201801 322
 
3.2%
201610 316
 
3.2%
201808 310
 
3.1%
201607 308
 
3.1%
201805 304
 
3.0%
201809 301
 
3.0%
201606 300
 
3.0%
201605 300
 
3.0%
201710 299
 
3.0%
201709 297
 
3.0%
Other values (24) 6943
69.4%
ValueCountFrequency (%)
201601 290
2.9%
201602 287
2.9%
201603 294
2.9%
201604 292
2.9%
201605 300
3.0%
201606 300
3.0%
201607 308
3.1%
201608 288
2.9%
201609 297
3.0%
201610 316
3.2%
ValueCountFrequency (%)
201810 291
2.9%
201809 301
3.0%
201808 310
3.1%
201807 287
2.9%
201806 289
2.9%
201805 304
3.0%
201804 282
2.8%
201803 296
3.0%
201802 293
2.9%
201801 322
3.2%

시군코드
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4135.5138
Minimum4111
Maximum4183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:32:05.365753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4111
5-th percentile4111
Q14119
median4128
Q34150
95-th percentile4167
Maximum4183
Range72
Interquartile range (IQR)31

Descriptive statistics

Standard deviation20.015031
Coefficient of variation (CV)0.0048397931
Kurtosis-0.64177729
Mean4135.5138
Median Absolute Deviation (MAD)15
Skewness0.61026141
Sum41355138
Variance400.60147
MonotonicityNot monotonic
2024-03-13T08:32:05.468772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4113 912
 
9.1%
4111 718
 
7.2%
4128 698
 
7.0%
4119 650
 
6.5%
4146 568
 
5.7%
4117 558
 
5.6%
4127 438
 
4.4%
4159 417
 
4.2%
4122 387
 
3.9%
4121 334
 
3.3%
Other values (21) 4320
43.2%
ValueCountFrequency (%)
4111 718
7.2%
4113 912
9.1%
4115 250
 
2.5%
4117 558
5.6%
4119 650
6.5%
4121 334
 
3.3%
4122 387
3.9%
4125 143
 
1.4%
4127 438
4.4%
4128 698
7.0%
ValueCountFrequency (%)
4183 225
2.2%
4182 96
 
1.0%
4180 167
1.7%
4167 220
2.2%
4165 256
2.6%
4163 205
2.1%
4161 196
2.0%
4159 417
4.2%
4157 226
2.3%
4155 273
2.7%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct1143
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5458975 × 109
Minimum41111560
Maximum4.183041 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:32:05.578049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41111560
5-th percentile41131561
Q141450520
median4.11716 × 109
Q34.1390593 × 109
95-th percentile4.1631544 × 109
Maximum4.183041 × 109
Range4.1419294 × 109
Interquartile range (IQR)4.0976088 × 109

Descriptive statistics

Standard deviation1.9956131 × 109
Coefficient of variation (CV)0.78385448
Kurtosis-1.7901009
Mean2.5458975 × 109
Median Absolute Deviation (MAD)39876000
Skewness-0.45828376
Sum2.5458975 × 1013
Variance3.9824718 × 1018
MonotonicityNot monotonic
2024-03-13T08:32:05.681379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41117520 24
 
0.2%
41830250 23
 
0.2%
41670360 20
 
0.2%
41111566 19
 
0.2%
41113520 19
 
0.2%
41820250 19
 
0.2%
41111597 19
 
0.2%
41820325 19
 
0.2%
41113690 18
 
0.2%
41113670 18
 
0.2%
Other values (1133) 9802
98.0%
ValueCountFrequency (%)
41111560 16
0.2%
41111566 19
0.2%
41111571 18
0.2%
41111572 16
0.2%
41111573 15
0.1%
41111580 18
0.2%
41111591 13
0.1%
41111597 19
0.2%
41111598 6
 
0.1%
41111600 17
0.2%
ValueCountFrequency (%)
4183041000 15
0.1%
4183040000 13
0.1%
4183039500 12
0.1%
4183038000 10
0.1%
4183037000 15
0.1%
4183036000 12
0.1%
4183035000 8
0.1%
4183034000 14
0.1%
4183033000 12
0.1%
4183031000 13
0.1%

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
성남시
912 
수원시
718 
고양시
698 
부천시
650 
용인시
 
568
Other values (26)
6454 

Length

Max length4
Median length3
Mean length3.0688
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row시흥시
2nd row안양시
3rd row가평군
4th row안양시
5th row용인시

Common Values

ValueCountFrequency (%)
성남시 912
 
9.1%
수원시 718
 
7.2%
고양시 698
 
7.0%
부천시 650
 
6.5%
용인시 568
 
5.7%
안양시 558
 
5.6%
안산시 438
 
4.4%
화성시 417
 
4.2%
평택시 387
 
3.9%
광명시 334
 
3.3%
Other values (21) 4320
43.2%

Length

2024-03-13T08:32:05.780109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성남시 912
 
9.1%
수원시 718
 
7.2%
고양시 698
 
7.0%
부천시 650
 
6.5%
용인시 568
 
5.7%
안양시 558
 
5.6%
안산시 438
 
4.4%
화성시 417
 
4.2%
평택시 387
 
3.9%
광명시 334
 
3.3%
Other values (21) 4320
43.2%
Distinct558
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T08:32:06.046678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.3902
Min length2

Characters and Unicode

Total characters33902
Distinct characters200
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row군자동
2nd row박달2동
3rd row설악면
4th row평안동
5th row마북동
ValueCountFrequency (%)
중앙동 138
 
1.4%
금곡동 51
 
0.5%
고등동 43
 
0.4%
부림동 40
 
0.4%
대야동 40
 
0.4%
풍산동 39
 
0.4%
정자1동 36
 
0.4%
위례동 35
 
0.4%
정자3동 34
 
0.3%
신장1동 34
 
0.3%
Other values (548) 9510
95.1%
2024-03-13T08:32:06.437963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7819
23.1%
1814
 
5.4%
1 1452
 
4.3%
2 1358
 
4.0%
673
 
2.0%
639
 
1.9%
3 638
 
1.9%
538
 
1.6%
532
 
1.6%
496
 
1.5%
Other values (190) 17943
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30119
88.8%
Decimal Number 3783
 
11.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7819
26.0%
1814
 
6.0%
673
 
2.2%
639
 
2.1%
538
 
1.8%
532
 
1.8%
496
 
1.6%
459
 
1.5%
431
 
1.4%
412
 
1.4%
Other values (181) 16306
54.1%
Decimal Number
ValueCountFrequency (%)
1 1452
38.4%
2 1358
35.9%
3 638
16.9%
4 177
 
4.7%
5 42
 
1.1%
6 42
 
1.1%
7 39
 
1.0%
8 22
 
0.6%
9 13
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30119
88.8%
Common 3783
 
11.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
7819
26.0%
1814
 
6.0%
673
 
2.2%
639
 
2.1%
538
 
1.8%
532
 
1.8%
496
 
1.6%
459
 
1.5%
431
 
1.4%
412
 
1.4%
Other values (181) 16306
54.1%
Common
ValueCountFrequency (%)
1 1452
38.4%
2 1358
35.9%
3 638
16.9%
4 177
 
4.7%
5 42
 
1.1%
6 42
 
1.1%
7 39
 
1.0%
8 22
 
0.6%
9 13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30119
88.8%
ASCII 3783
 
11.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
7819
26.0%
1814
 
6.0%
673
 
2.2%
639
 
2.1%
538
 
1.8%
532
 
1.8%
496
 
1.6%
459
 
1.5%
431
 
1.4%
412
 
1.4%
Other values (181) 16306
54.1%
ASCII
ValueCountFrequency (%)
1 1452
38.4%
2 1358
35.9%
3 638
16.9%
4 177
 
4.7%
5 42
 
1.1%
6 42
 
1.1%
7 39
 
1.0%
8 22
 
0.6%
9 13
 
0.3%

총인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct8864
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22852.194
Minimum163
Maximum104470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:32:06.574210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum163
5-th percentile3641.85
Q111623.5
median20464.5
Q330385.75
95-th percentile50748.8
Maximum104470
Range104307
Interquartile range (IQR)18762.25

Descriptive statistics

Standard deviation15432.821
Coefficient of variation (CV)0.67533214
Kurtosis2.9650627
Mean22852.194
Median Absolute Deviation (MAD)9302
Skewness1.3226947
Sum2.2852194 × 108
Variance2.3817198 × 108
MonotonicityNot monotonic
2024-03-13T08:32:06.693273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172 5
 
0.1%
5154 4
 
< 0.1%
20078 4
 
< 0.1%
10537 4
 
< 0.1%
8062 4
 
< 0.1%
3108 4
 
< 0.1%
6687 3
 
< 0.1%
25566 3
 
< 0.1%
5055 3
 
< 0.1%
14535 3
 
< 0.1%
Other values (8854) 9963
99.6%
ValueCountFrequency (%)
163 1
 
< 0.1%
164 1
 
< 0.1%
165 1
 
< 0.1%
167 2
 
< 0.1%
169 3
< 0.1%
171 1
 
< 0.1%
172 5
0.1%
173 2
 
< 0.1%
174 1
 
< 0.1%
175 2
 
< 0.1%
ValueCountFrequency (%)
104470 1
< 0.1%
103521 1
< 0.1%
102108 1
< 0.1%
101652 1
< 0.1%
101327 1
< 0.1%
100998 1
< 0.1%
99716 1
< 0.1%
99264 1
< 0.1%
98836 1
< 0.1%
97823 1
< 0.1%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct7360
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9046.8108
Minimum70
Maximum41549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:32:06.800253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile1781
Q14865.75
median8278
Q312176.5
95-th percentile18898.2
Maximum41549
Range41479
Interquartile range (IQR)7310.75

Descriptive statistics

Standard deviation5650.3264
Coefficient of variation (CV)0.62456555
Kurtosis3.5473098
Mean9046.8108
Median Absolute Deviation (MAD)3630.5
Skewness1.3313755
Sum90468108
Variance31926188
MonotonicityNot monotonic
2024-03-13T08:32:07.113497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5763 6
 
0.1%
5618 6
 
0.1%
8915 5
 
0.1%
10406 5
 
0.1%
12718 5
 
0.1%
7390 5
 
0.1%
4686 5
 
0.1%
10068 5
 
0.1%
8881 5
 
0.1%
10062 5
 
0.1%
Other values (7350) 9948
99.5%
ValueCountFrequency (%)
70 1
 
< 0.1%
71 4
< 0.1%
72 1
 
< 0.1%
73 3
< 0.1%
74 3
< 0.1%
75 4
< 0.1%
76 2
< 0.1%
78 1
 
< 0.1%
88 1
 
< 0.1%
103 1
 
< 0.1%
ValueCountFrequency (%)
41549 1
< 0.1%
41125 1
< 0.1%
40938 1
< 0.1%
40724 1
< 0.1%
40715 1
< 0.1%
40473 1
< 0.1%
40083 1
< 0.1%
39930 1
< 0.1%
39894 1
< 0.1%
39777 1
< 0.1%

세대당인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct190
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.438877
Minimum1.3
Maximum3.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:32:07.229998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.9
Q12.17
median2.46
Q32.69
95-th percentile3.01
Maximum3.41
Range2.11
Interquartile range (IQR)0.52

Descriptive statistics

Standard deviation0.35153686
Coefficient of variation (CV)0.14413882
Kurtosis-0.54503066
Mean2.438877
Median Absolute Deviation (MAD)0.27
Skewness0.028804037
Sum24388.77
Variance0.12357817
MonotonicityNot monotonic
2024-03-13T08:32:07.346424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.52 139
 
1.4%
2.53 133
 
1.3%
2.54 131
 
1.3%
2.2 125
 
1.2%
2.64 123
 
1.2%
2.55 120
 
1.2%
2.5 120
 
1.2%
2.47 119
 
1.2%
2.51 117
 
1.2%
2.61 113
 
1.1%
Other values (180) 8760
87.6%
ValueCountFrequency (%)
1.3 1
 
< 0.1%
1.31 12
0.1%
1.32 5
0.1%
1.46 3
 
< 0.1%
1.55 3
 
< 0.1%
1.56 5
0.1%
1.57 2
 
< 0.1%
1.58 1
 
< 0.1%
1.59 5
0.1%
1.6 8
0.1%
ValueCountFrequency (%)
3.41 2
 
< 0.1%
3.4 1
 
< 0.1%
3.39 3
 
< 0.1%
3.38 11
0.1%
3.37 1
 
< 0.1%
3.36 3
 
< 0.1%
3.35 8
0.1%
3.34 2
 
< 0.1%
3.33 2
 
< 0.1%
3.32 5
0.1%

남성인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct7911
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11495.988
Minimum87
Maximum52717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:32:07.453123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum87
5-th percentile1887.95
Q15984
median10241
Q315253.5
95-th percentile25152.15
Maximum52717
Range52630
Interquartile range (IQR)9269.5

Descriptive statistics

Standard deviation7688.9074
Coefficient of variation (CV)0.66883397
Kurtosis3.2121004
Mean11495.988
Median Absolute Deviation (MAD)4503.5
Skewness1.3613032
Sum1.1495988 × 108
Variance59119298
MonotonicityNot monotonic
2024-03-13T08:32:07.580835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8993 6
 
0.1%
2870 5
 
0.1%
8266 5
 
0.1%
2458 5
 
0.1%
929 5
 
0.1%
6146 5
 
0.1%
2224 5
 
0.1%
2873 5
 
0.1%
12259 4
 
< 0.1%
10237 4
 
< 0.1%
Other values (7901) 9951
99.5%
ValueCountFrequency (%)
87 2
< 0.1%
88 1
 
< 0.1%
89 1
 
< 0.1%
90 1
 
< 0.1%
91 4
< 0.1%
92 3
< 0.1%
93 4
< 0.1%
94 3
< 0.1%
114 1
 
< 0.1%
115 1
 
< 0.1%
ValueCountFrequency (%)
52717 1
< 0.1%
52176 1
< 0.1%
51431 1
< 0.1%
51307 1
< 0.1%
51190 1
< 0.1%
51025 1
< 0.1%
50697 1
< 0.1%
50482 1
< 0.1%
50255 1
< 0.1%
49753 1
< 0.1%

여성인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct7966
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11356.206
Minimum76
Maximum51753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:32:07.682832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile1706
Q15577.5
median10068
Q315331.5
95-th percentile25697.9
Maximum51753
Range51677
Interquartile range (IQR)9754

Descriptive statistics

Standard deviation7764.6857
Coefficient of variation (CV)0.68373942
Kurtosis2.721237
Mean11356.206
Median Absolute Deviation (MAD)4760.5
Skewness1.2851841
Sum1.1356206 × 108
Variance60290344
MonotonicityNot monotonic
2024-03-13T08:32:07.791633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12359 6
 
0.1%
78 5
 
0.1%
2883 5
 
0.1%
3804 5
 
0.1%
81 5
 
0.1%
837 5
 
0.1%
13996 5
 
0.1%
7129 5
 
0.1%
793 4
 
< 0.1%
5286 4
 
< 0.1%
Other values (7956) 9951
99.5%
ValueCountFrequency (%)
76 1
 
< 0.1%
77 4
< 0.1%
78 5
0.1%
79 1
 
< 0.1%
80 3
< 0.1%
81 5
0.1%
82 4
< 0.1%
86 3
< 0.1%
87 2
 
< 0.1%
92 3
< 0.1%
ValueCountFrequency (%)
51753 1
< 0.1%
51345 1
< 0.1%
50677 1
< 0.1%
50462 1
< 0.1%
50302 1
< 0.1%
49691 1
< 0.1%
49019 1
< 0.1%
48782 1
< 0.1%
48744 1
< 0.1%
48581 1
< 0.1%

남성여성비율
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.032721
Minimum0.85
Maximum1.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:32:07.901406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile0.93
Q10.97
median1.01
Q31.07
95-th percentile1.23
Maximum1.74
Range0.89
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.098729534
Coefficient of variation (CV)0.095601362
Kurtosis7.4079955
Mean1.032721
Median Absolute Deviation (MAD)0.05
Skewness2.1960257
Sum10327.21
Variance0.0097475209
MonotonicityNot monotonic
2024-03-13T08:32:08.006616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.01 688
 
6.9%
0.98 642
 
6.4%
0.97 635
 
6.3%
0.96 585
 
5.9%
0.95 576
 
5.8%
1.0 562
 
5.6%
0.99 554
 
5.5%
1.02 547
 
5.5%
1.03 502
 
5.0%
1.04 446
 
4.5%
Other values (75) 4263
42.6%
ValueCountFrequency (%)
0.85 2
 
< 0.1%
0.86 9
 
0.1%
0.87 3
 
< 0.1%
0.88 17
 
0.2%
0.89 44
 
0.4%
0.9 65
 
0.7%
0.91 84
 
0.8%
0.92 141
 
1.4%
0.93 273
2.7%
0.94 417
4.2%
ValueCountFrequency (%)
1.74 1
 
< 0.1%
1.73 1
 
< 0.1%
1.68 1
 
< 0.1%
1.66 1
 
< 0.1%
1.65 4
< 0.1%
1.64 4
< 0.1%
1.63 4
< 0.1%
1.62 2
< 0.1%
1.61 2
< 0.1%
1.6 2
< 0.1%

동단위분석시작연월
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

동단위분석종료연월
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

동단위분석시작년도
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

동단위분석종료년도
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

마트완료여부
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

생성일자
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20180323
3548 
20181120
3477 
20190131
2975 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20190131
2nd row20181120
3rd row20180323
4th row20180323
5th row20181120

Common Values

ValueCountFrequency (%)
20180323 3548
35.5%
20181120 3477
34.8%
20190131 2975
29.8%

Length

2024-03-13T08:32:08.101753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:32:08.180276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20180323 3548
35.5%
20181120 3477
34.8%
20190131 2975
29.8%

Interactions

2024-03-13T08:32:04.101873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:58.368082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:59.061752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:59.754929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:00.455929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:01.115718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:01.779006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:02.709405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:03.399131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:04.169613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:58.438578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:59.142407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:59.827448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:00.524353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:01.189763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:02.067304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:02.788347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:03.496721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:04.248775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:58.520650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:59.225691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:59.905146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:00.602418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:01.277605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:02.175430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:02.888001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:03.581482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:04.327026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:58.597182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:59.305477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:59.988717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:00.676801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:01.351710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:02.263224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:02.964969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:03.658383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:04.417920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:58.672045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:59.382661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:00.088219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:00.751378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:01.423279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:02.336244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:03.041190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:03.734844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:04.498584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:58.745337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:59.452944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:00.158709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:00.823676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:01.496722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:02.414405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:03.110472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:03.805640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:04.570614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:58.828109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:59.525537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:00.244734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:00.895847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:01.567062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:02.493241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:03.187592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:03.881136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:04.654290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:58.907110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:59.602135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:00.318640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:00.967225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:01.641371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:02.569338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:03.258996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:03.957414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:04.731565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:58.983102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:59.682635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:00.388128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:01.039650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:01.715765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:02.643967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:03.329369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:04.033985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:32:08.241182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연월시군코드행정동코드시군명총인구수세대수세대당인구수남성인구수여성인구수남성여성비율생성일자
기준연월1.0000.0000.9940.0000.0330.0440.0910.0210.0290.0431.000
시군코드0.0001.0000.0991.0000.4640.4210.4090.4540.4630.3930.000
행정동코드0.9940.0991.0000.2220.0660.0760.0790.0460.0630.0690.676
시군명0.0001.0000.2221.0000.6670.6460.6120.6590.6640.6340.000
총인구수0.0330.4640.0660.6671.0000.9670.6300.9970.9980.5060.053
세대수0.0440.4210.0760.6460.9671.0000.5730.9750.9610.4760.063
세대당인구수0.0910.4090.0790.6120.6300.5731.0000.6190.6400.7750.118
남성인구수0.0210.4540.0460.6590.9970.9750.6191.0000.9920.4850.040
여성인구수0.0290.4630.0630.6640.9980.9610.6400.9921.0000.5270.046
남성여성비율0.0430.3930.0690.6340.5060.4760.7750.4850.5271.0000.060
생성일자1.0000.0000.6760.0000.0530.0630.1180.0400.0460.0601.000
2024-03-13T08:32:08.335408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생성일자시군명
생성일자1.0000.000
시군명0.0001.000
2024-03-13T08:32:08.406048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연월시군코드행정동코드총인구수세대수세대당인구수남성인구수여성인구수남성여성비율시군명생성일자
기준연월1.0000.0180.650-0.0060.016-0.088-0.005-0.0070.0170.0001.000
시군코드0.0181.0000.547-0.194-0.186-0.206-0.183-0.2050.3310.9990.000
행정동코드0.6500.5471.000-0.097-0.085-0.129-0.090-0.1030.1660.1930.931
총인구수-0.006-0.194-0.0971.0000.9790.6080.9980.998-0.4400.3030.031
세대수0.016-0.186-0.0850.9791.0000.4510.9840.970-0.3450.2880.037
세대당인구수-0.088-0.206-0.1290.6080.4511.0000.5850.630-0.6550.2650.070
남성인구수-0.005-0.183-0.0900.9980.9840.5851.0000.993-0.4000.2980.023
여성인구수-0.007-0.205-0.1030.9980.9700.6300.9931.000-0.4800.3010.027
남성여성비율0.0170.3310.166-0.440-0.345-0.655-0.400-0.4801.0000.2800.035
시군명0.0000.9990.1930.3030.2880.2650.2980.3010.2801.0000.000
생성일자1.0000.0000.9310.0310.0370.0700.0230.0270.0350.0001.000

Missing values

2024-03-13T08:32:04.838576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:32:05.027570image/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

기준연월시군코드행정동코드시군명행정동명총인구수세대수세대당인구수남성인구수여성인구수남성여성비율동단위분석시작연월동단위분석종료연월동단위분석시작년도동단위분석종료년도마트완료여부생성일자
505620180241394139058100시흥시군자동24957100382.4813044119131.09<NA><NA><NA><NA><NA>20190131
606820171241174117163000안양시박달2동2378779462.9911930118571.0<NA><NA><NA><NA><NA>20181120
13957201610418241820310가평군설악면893843882.03455143871.03<NA><NA><NA><NA><NA>20180323
17267201604411741173576안양시평안동2577682193.1312746130300.97<NA><NA><NA><NA><NA>20180323
1070920170341464146357000용인시마북동32457115582.815917165400.96<NA><NA><NA><NA><NA>20181120
1108620170341194119058000부천시역곡2동2032481032.510018103060.97<NA><NA><NA><NA><NA>20181120
444120180341484148031000파주시월롱면1172889441.31731144171.65<NA><NA><NA><NA><NA>20190131
685520171041214121055000광명시광명4동1495364182.32730176520.95<NA><NA><NA><NA><NA>20181120
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722420171041274127359000안산시선부2동22216102892.1512042101741.18<NA><NA><NA><NA><NA>20181120
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13766201610414641461320용인시남사면704031902.2371833221.11<NA><NA><NA><NA><NA>20180323