Overview

Dataset statistics

Number of variables13
Number of observations44
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory117.0 B

Variable types

Categorical1
Text1
Numeric10
DateTime1

Dataset

Description경기도 수원시 월별 인구현황으로 월별 구별, 동별, 세대 수, 인구 현황(남, 여, 전월말 인구수, 인구 증감) 외국인 현황 등에 대한 데이터입니다.
Author경기도 수원시
URLhttps://www.data.go.kr/data/15051539/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
세대수 is highly overall correlated with 인구_합계 and 4 other fieldsHigh correlation
인구_합계 is highly overall correlated with 세대수 and 4 other fieldsHigh correlation
인구_내국인_인구수_계 is highly overall correlated with 세대수 and 4 other fieldsHigh correlation
인구_내국인_인구수_남 is highly overall correlated with 세대수 and 4 other fieldsHigh correlation
인구_내국인_인구수_여 is highly overall correlated with 세대수 and 4 other fieldsHigh correlation
인구_내국인_전월말인구수 is highly overall correlated with 세대수 and 4 other fieldsHigh correlation
외국인_계 is highly overall correlated with 외국인_남 and 1 other fieldsHigh correlation
외국인_남 is highly overall correlated with 외국인_계 and 1 other fieldsHigh correlation
외국인_여 is highly overall correlated with 외국인_계 and 1 other fieldsHigh correlation
구_동 has unique valuesUnique
세대수 has unique valuesUnique
인구_합계 has unique valuesUnique
인구_내국인_인구수_계 has unique valuesUnique
인구_내국인_인구수_남 has unique valuesUnique
인구_내국인_인구수_여 has unique valuesUnique
인구_내국인_전월말인구수 has unique valuesUnique
외국인_계 has unique valuesUnique
외국인_남 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:56:30.476353
Analysis finished2023-12-12 14:56:41.067877
Duration10.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구
Categorical

Distinct4
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size484.0 B
권선구
12 
영통구
12 
장안구
10 
팔달구
10 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
권선구 12
27.3%
영통구 12
27.3%
장안구 10
22.7%
팔달구 10
22.7%

Length

2023-12-12T23:56:41.132256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:56:41.263023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
권선구 12
27.3%
영통구 12
27.3%
장안구 10
22.7%
팔달구 10
22.7%

구_동
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2023-12-12T23:56:41.513463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.5909091
Min length2

Characters and Unicode

Total characters158
Distinct characters52
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

Unique44 ?
Unique (%)100.0%

Sample

1st row파장동
2nd row율천동
3rd row정자1동
4th row정자2동
5th row정자3동
ValueCountFrequency (%)
파장동 1
 
2.2%
인계동 1
 
2.2%
매교동 1
 
2.2%
매탄1동 1
 
2.2%
매산동 1
 
2.2%
고등동 1
 
2.2%
화서1동 1
 
2.2%
화서2동 1
 
2.2%
지동 1
 
2.2%
우만1동 1
 
2.2%
Other values (36) 36
78.3%
2023-12-12T23:56:41.908022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 44
27.8%
1 10
 
6.3%
2 10
 
6.3%
매 7
 
4.4%
영 4
 
2.5%
탄 4
 
2.5%
3 4
 
2.5%
원 3
 
1.9%
서 3
 
1.9%
선 3
 
1.9%
Other values (42) 66
41.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 131
82.9%
Decimal Number 25
 
15.8%
Space Separator 2
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 44
33.6%
매 7
 
5.3%
영 4
 
3.1%
탄 4
 
3.1%
원 3
 
2.3%
서 3
 
2.3%
선 3
 
2.3%
통 3
 
2.3%
화 3
 
2.3%
교 3
 
2.3%
Other values (37) 54
41.2%
Decimal Number
ValueCountFrequency (%)
1 10
40.0%
2 10
40.0%
3 4
 
16.0%
4 1
 
4.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 131
82.9%
Common 27
 
17.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 44
33.6%
매 7
 
5.3%
영 4
 
3.1%
탄 4
 
3.1%
원 3
 
2.3%
서 3
 
2.3%
선 3
 
2.3%
통 3
 
2.3%
화 3
 
2.3%
교 3
 
2.3%
Other values (37) 54
41.2%
Common
ValueCountFrequency (%)
1 10
37.0%
2 10
37.0%
3 4
 
14.8%
2
 
7.4%
4 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 131
82.9%
ASCII 27
 
17.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 44
33.6%
매 7
 
5.3%
영 4
 
3.1%
탄 4
 
3.1%
원 3
 
2.3%
서 3
 
2.3%
선 3
 
2.3%
통 3
 
2.3%
화 3
 
2.3%
교 3
 
2.3%
Other values (37) 54
41.2%
ASCII
ValueCountFrequency (%)
1 10
37.0%
2 10
37.0%
3 4
 
14.8%
2
 
7.4%
4 1
 
3.7%

세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11642.545
Minimum3308
Maximum21872
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:56:42.031016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3308
5-th percentile5816.1
Q18782.75
median10851
Q314362
95-th percentile20003.9
Maximum21872
Range18564
Interquartile range (IQR)5579.25

Descriptive statistics

Standard deviation4528.7108
Coefficient of variation (CV)0.38897944
Kurtosis-0.3050587
Mean11642.545
Median Absolute Deviation (MAD)2831
Skewness0.45547639
Sum512272
Variance20509221
MonotonicityNot monotonic
2023-12-12T23:56:42.149492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
10686 1
 
2.3%
3308 1
 
2.3%
8991 1
 
2.3%
9904 1
 
2.3%
9683 1
 
2.3%
5766 1
 
2.3%
10794 1
 
2.3%
7602 1
 
2.3%
21528 1
 
2.3%
7722 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
3308 1
2.3%
4445 1
2.3%
5766 1
2.3%
6100 1
2.3%
6184 1
2.3%
6529 1
2.3%
6577 1
2.3%
6654 1
2.3%
7602 1
2.3%
7722 1
2.3%
ValueCountFrequency (%)
21872 1
2.3%
21528 1
2.3%
20351 1
2.3%
18037 1
2.3%
17294 1
2.3%
17285 1
2.3%
17169 1
2.3%
16777 1
2.3%
16535 1
2.3%
15684 1
2.3%

인구_합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27700.477
Minimum6715
Maximum53404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:56:42.269550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6715
5-th percentile11636.7
Q119692.5
median26152.5
Q336077.75
95-th percentile46553.6
Maximum53404
Range46689
Interquartile range (IQR)16385.25

Descriptive statistics

Standard deviation11358.394
Coefficient of variation (CV)0.41004328
Kurtosis-0.5523968
Mean27700.477
Median Absolute Deviation (MAD)7688
Skewness0.39911915
Sum1218821
Variance1.2901312 × 108
MonotonicityNot monotonic
2023-12-12T23:56:42.375160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
23833 1
 
2.3%
6715 1
 
2.3%
21931 1
 
2.3%
24227 1
 
2.3%
26556 1
 
2.3%
12179 1
 
2.3%
20842 1
 
2.3%
17372 1
 
2.3%
40581 1
 
2.3%
19205 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
6715 1
2.3%
11482 1
2.3%
11541 1
2.3%
12179 1
2.3%
12675 1
2.3%
15382 1
2.3%
16841 1
2.3%
17097 1
2.3%
17372 1
2.3%
18937 1
2.3%
ValueCountFrequency (%)
53404 1
2.3%
49131 1
2.3%
46673 1
2.3%
45877 1
2.3%
45076 1
2.3%
42383 1
2.3%
41380 1
2.3%
40581 1
2.3%
39858 1
2.3%
38662 1
2.3%

인구_내국인_인구수_계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26930.045
Minimum5928
Maximum52990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:56:42.480567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5928
5-th percentile10496.7
Q118982.75
median25562.5
Q335559
95-th percentile46310.35
Maximum52990
Range47062
Interquartile range (IQR)16576.25

Descriptive statistics

Standard deviation11498.02
Coefficient of variation (CV)0.42695881
Kurtosis-0.54271463
Mean26930.045
Median Absolute Deviation (MAD)7433
Skewness0.38388565
Sum1184922
Variance1.3220446 × 108
MonotonicityNot monotonic
2023-12-12T23:56:42.588985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
23486 1
 
2.3%
5928 1
 
2.3%
19082 1
 
2.3%
22575 1
 
2.3%
26384 1
 
2.3%
11013 1
 
2.3%
20248 1
 
2.3%
17070 1
 
2.3%
39398 1
 
2.3%
17948 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
5928 1
2.3%
10272 1
2.3%
10464 1
2.3%
10682 1
2.3%
11013 1
2.3%
14934 1
2.3%
16086 1
2.3%
16738 1
2.3%
17070 1
2.3%
17948 1
2.3%
ValueCountFrequency (%)
52990 1
2.3%
48878 1
2.3%
46435 1
2.3%
45604 1
2.3%
43156 1
2.3%
42203 1
2.3%
40552 1
2.3%
39398 1
2.3%
38885 1
2.3%
37756 1
2.3%

인구_내국인_인구수_남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13562.705
Minimum3127
Maximum25965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:56:42.694140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3127
5-th percentile5596.85
Q19462.5
median12819.5
Q317711.25
95-th percentile23081.9
Maximum25965
Range22838
Interquartile range (IQR)8248.75

Descriptive statistics

Standard deviation5735.5693
Coefficient of variation (CV)0.42289274
Kurtosis-0.67942069
Mean13562.705
Median Absolute Deviation (MAD)3862
Skewness0.35895304
Sum596759
Variance32896756
MonotonicityNot monotonic
2023-12-12T23:56:42.805732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
11835 1
 
2.3%
3127 1
 
2.3%
9630 1
 
2.3%
10996 1
 
2.3%
12982 1
 
2.3%
5613 1
 
2.3%
10105 1
 
2.3%
8570 1
 
2.3%
19907 1
 
2.3%
9021 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
3127 1
2.3%
5147 1
2.3%
5594 1
2.3%
5613 1
2.3%
5704 1
2.3%
7407 1
2.3%
8285 1
2.3%
8484 1
2.3%
8570 1
2.3%
9021 1
2.3%
ValueCountFrequency (%)
25965 1
2.3%
24022 1
2.3%
23114 1
2.3%
22900 1
2.3%
22545 1
2.3%
21085 1
2.3%
20531 1
2.3%
19907 1
2.3%
19688 1
2.3%
19240 1
2.3%

인구_내국인_인구수_여
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13367.341
Minimum2801
Maximum27025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:56:43.243735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2801
5-th percentile5000.05
Q19446
median12259.5
Q317587.25
95-th percentile23281.7
Maximum27025
Range24224
Interquartile range (IQR)8141.25

Descriptive statistics

Standard deviation5786.3918
Coefficient of variation (CV)0.43287531
Kurtosis-0.38446895
Mean13367.341
Median Absolute Deviation (MAD)3546
Skewness0.42018876
Sum588163
Variance33482330
MonotonicityNot monotonic
2023-12-12T23:56:43.458487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
11651 1
 
2.3%
2801 1
 
2.3%
9452 1
 
2.3%
11579 1
 
2.3%
13402 1
 
2.3%
5400 1
 
2.3%
10143 1
 
2.3%
8500 1
 
2.3%
19491 1
 
2.3%
8927 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
2801 1
2.3%
4870 1
2.3%
4978 1
2.3%
5125 1
2.3%
5400 1
2.3%
7527 1
2.3%
7801 1
2.3%
8254 1
2.3%
8500 1
2.3%
8927 1
2.3%
ValueCountFrequency (%)
27025 1
2.3%
24856 1
2.3%
23321 1
2.3%
23059 1
2.3%
21118 1
2.3%
20256 1
2.3%
20021 1
2.3%
19491 1
2.3%
19197 1
2.3%
18516 1
2.3%

인구_내국인_전월말인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27715.136
Minimum6724
Maximum53420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:56:43.602360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6724
5-th percentile11635.3
Q119708.25
median26194.5
Q336135.5
95-th percentile46551.35
Maximum53420
Range46696
Interquartile range (IQR)16427.25

Descriptive statistics

Standard deviation11367.265
Coefficient of variation (CV)0.41014646
Kurtosis-0.55293906
Mean27715.136
Median Absolute Deviation (MAD)7732
Skewness0.39981041
Sum1219466
Variance1.2921471 × 108
MonotonicityNot monotonic
2023-12-12T23:56:43.754184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
23801 1
 
2.3%
6724 1
 
2.3%
21445 1
 
2.3%
24237 1
 
2.3%
26588 1
 
2.3%
12249 1
 
2.3%
20921 1
 
2.3%
17385 1
 
2.3%
40380 1
 
2.3%
19253 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
6724 1
2.3%
11458 1
2.3%
11527 1
2.3%
12249 1
2.3%
12709 1
2.3%
15387 1
2.3%
16836 1
2.3%
17096 1
2.3%
17385 1
2.3%
18959 1
2.3%
ValueCountFrequency (%)
53420 1
2.3%
49233 1
2.3%
46667 1
2.3%
45896 1
2.3%
45165 1
2.3%
42461 1
2.3%
41459 1
2.3%
40380 1
2.3%
39855 1
2.3%
38643 1
2.3%

인구_내국인_인구증감전월대비
Real number (ℝ)

Distinct40
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-14.659091
Minimum-110
Maximum486
Zeros0
Zeros (%)0.0%
Negative31
Negative (%)70.5%
Memory size528.0 B
2023-12-12T23:56:43.878975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-110
5-th percentile-94.95
Q1-65
median-20.5
Q33.5
95-th percentile45.6
Maximum486
Range596
Interquartile range (IQR)68.5

Descriptive statistics

Standard deviation93.481983
Coefficient of variation (CV)-6.3770655
Kurtosis19.659633
Mean-14.659091
Median Absolute Deviation (MAD)33
Skewness3.9243716
Sum-645
Variance8738.8811
MonotonicityNot monotonic
2023-12-12T23:56:44.055568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
-79 2
 
4.5%
-65 2
 
4.5%
-9 2
 
4.5%
-5 2
 
4.5%
32 1
 
2.3%
-10 1
 
2.3%
-32 1
 
2.3%
-70 1
 
2.3%
-13 1
 
2.3%
201 1
 
2.3%
Other values (30) 30
68.2%
ValueCountFrequency (%)
-110 1
2.3%
-102 1
2.3%
-96 1
2.3%
-89 1
2.3%
-80 1
2.3%
-79 2
4.5%
-78 1
2.3%
-70 1
2.3%
-67 1
2.3%
-65 2
4.5%
ValueCountFrequency (%)
486 1
2.3%
201 1
2.3%
48 1
2.3%
32 1
2.3%
24 1
2.3%
19 1
2.3%
15 1
2.3%
14 1
2.3%
9 1
2.3%
6 1
2.3%

외국인_계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean770.43182
Minimum33
Maximum2849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:56:44.207400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile157.55
Q1268
median505.5
Q31170.25
95-th percentile2167.35
Maximum2849
Range2816
Interquartile range (IQR)902.25

Descriptive statistics

Standard deviation664.63676
Coefficient of variation (CV)0.86268083
Kurtosis1.2917692
Mean770.43182
Median Absolute Deviation (MAD)326
Skewness1.314423
Sum33899
Variance441742.02
MonotonicityNot monotonic
2023-12-12T23:56:44.330826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
347 1
 
2.3%
787 1
 
2.3%
2849 1
 
2.3%
1652 1
 
2.3%
172 1
 
2.3%
1166 1
 
2.3%
594 1
 
2.3%
302 1
 
2.3%
1183 1
 
2.3%
1257 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
33 1
2.3%
103 1
2.3%
155 1
2.3%
172 1
2.3%
176 1
2.3%
179 1
2.3%
180 1
2.3%
181 1
2.3%
238 1
2.3%
246 1
2.3%
ValueCountFrequency (%)
2849 1
2.3%
2224 1
2.3%
2211 1
2.3%
1920 1
2.3%
1738 1
2.3%
1652 1
2.3%
1499 1
2.3%
1257 1
2.3%
1226 1
2.3%
1210 1
2.3%

외국인_남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean394.47727
Minimum9
Maximum1562
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:56:44.470086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile69.3
Q1116.5
median243
Q3577.5
95-th percentile1184.8
Maximum1562
Range1553
Interquartile range (IQR)461

Descriptive statistics

Standard deviation369.18858
Coefficient of variation (CV)0.93589316
Kurtosis1.6459431
Mean394.47727
Median Absolute Deviation (MAD)163.5
Skewness1.4431811
Sum17357
Variance136300.21
MonotonicityNot monotonic
2023-12-12T23:56:44.628604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
160 1
 
2.3%
403 1
 
2.3%
1562 1
 
2.3%
885 1
 
2.3%
71 1
 
2.3%
620 1
 
2.3%
299 1
 
2.3%
164 1
 
2.3%
588 1
 
2.3%
574 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
9 1
2.3%
42 1
2.3%
69 1
2.3%
71 1
2.3%
79 1
2.3%
80 1
2.3%
85 1
2.3%
86 1
2.3%
88 1
2.3%
108 1
2.3%
ValueCountFrequency (%)
1562 1
2.3%
1223 1
2.3%
1189 1
2.3%
1161 1
2.3%
914 1
2.3%
885 1
2.3%
768 1
2.3%
627 1
2.3%
620 1
2.3%
606 1
2.3%

외국인_여
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean375.95455
Minimum24
Maximum1287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:56:44.786124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile86.9
Q1151
median268
Q3593.5
95-th percentile963.4
Maximum1287
Range1263
Interquartile range (IQR)442.5

Descriptive statistics

Standard deviation299.09227
Coefficient of variation (CV)0.79555433
Kurtosis0.86734839
Mean375.95455
Median Absolute Deviation (MAD)167
Skewness1.170914
Sum16542
Variance89456.184
MonotonicityNot monotonic
2023-12-12T23:56:44.933742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
101 2
 
4.5%
187 1
 
2.3%
269 1
 
2.3%
1287 1
 
2.3%
767 1
 
2.3%
546 1
 
2.3%
295 1
 
2.3%
138 1
 
2.3%
595 1
 
2.3%
683 1
 
2.3%
Other values (33) 33
75.0%
ValueCountFrequency (%)
24 1
2.3%
61 1
2.3%
86 1
2.3%
92 1
2.3%
93 1
2.3%
97 1
2.3%
101 2
4.5%
129 1
2.3%
138 1
2.3%
145 1
2.3%
ValueCountFrequency (%)
1287 1
2.3%
1035 1
2.3%
988 1
2.3%
824 1
2.3%
767 1
2.3%
759 1
2.3%
731 1
2.3%
683 1
2.3%
604 1
2.3%
599 1
2.3%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
Minimum2021-05-31 00:00:00
Maximum2021-05-31 00:00:00
2023-12-12T23:56:45.071065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:45.172118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T23:56:39.808596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:31.215065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:32.264052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:33.063294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:33.830302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:34.770853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:35.676739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:36.533472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:37.812952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:38.789296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:39.907969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:31.334898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:32.358484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:33.140476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:33.954915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:34.870681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:35.773071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:36.651662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:37.930198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:38.922557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:39.984931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:31.448459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:32.440860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:33.202108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:34.039216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:34.965934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:35.865472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:36.734243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:38.030181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:39.006512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:40.080420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:31.546318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:32.519978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:33.271296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:34.110122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:35.047394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:35.949493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:36.809837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:38.141669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:39.088461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:40.190292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:31.646714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:32.595159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:33.338201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:34.186113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:35.134655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:36.024925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:36.907081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:38.224737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:39.184176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:40.283354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:31.744406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:32.666128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:33.411523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:34.271683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:35.225259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:36.114601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:36.990527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:38.315589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:39.279309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:40.389100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:31.840755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:32.734425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:33.496098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:34.344075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:35.303907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:36.188746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:37.398961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:38.404363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:39.396152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:40.475150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:31.952395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:32.812459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:33.580737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:34.469473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:35.401248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:36.282164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:37.485754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:38.499938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:39.496378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:40.554477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:32.042902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:32.900256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:33.650961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:34.589932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:35.488663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:36.361148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:37.572791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:38.590454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:39.588331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:40.656384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:32.166414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:32.985672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:33.732680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:34.684115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:35.593388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:36.444966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:37.687662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:38.690755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:39.711230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:56:45.287784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구구_동세대수인구_합계인구_내국인_인구수_계인구_내국인_인구수_남인구_내국인_인구수_여인구_내국인_전월말인구수인구_내국인_인구증감전월대비외국인_계외국인_남외국인_여
구1.0001.0000.0000.0000.0000.0880.2820.0000.0000.0630.0000.326
구_동1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
세대수0.0001.0001.0000.8340.7430.8200.6780.8380.4510.0000.0000.000
인구_합계0.0001.0000.8341.0000.9600.9600.9341.0000.0000.3530.5570.187
인구_내국인_인구수_계0.0001.0000.7430.9601.0000.9860.9930.9700.0000.0000.3420.000
인구_내국인_인구수_남0.0881.0000.8200.9600.9861.0000.9700.9520.0000.2380.4380.125
인구_내국인_인구수_여0.2821.0000.6780.9340.9930.9701.0000.9430.4340.1830.1140.252
인구_내국인_전월말인구수0.0001.0000.8381.0000.9700.9520.9431.0000.0000.0000.4840.000
인구_내국인_인구증감전월대비0.0001.0000.4510.0000.0000.0000.4340.0001.0000.6520.6560.787
외국인_계0.0631.0000.0000.3530.0000.2380.1830.0000.6521.0000.9270.936
외국인_남0.0001.0000.0000.5570.3420.4380.1140.4840.6560.9271.0000.865
외국인_여0.3261.0000.0000.1870.0000.1250.2520.0000.7870.9360.8651.000
2023-12-12T23:56:45.438296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수인구_합계인구_내국인_인구수_계인구_내국인_인구수_남인구_내국인_인구수_여인구_내국인_전월말인구수인구_내국인_인구증감전월대비외국인_계외국인_남외국인_여구
세대수1.0000.9440.9440.9540.9300.945-0.187-0.046-0.062-0.0270.000
인구_합계0.9441.0000.9970.9950.9921.000-0.191-0.206-0.217-0.1830.000
인구_내국인_인구수_계0.9440.9971.0000.9970.9970.998-0.193-0.243-0.253-0.2200.000
인구_내국인_인구수_남0.9540.9950.9971.0000.9930.995-0.199-0.227-0.237-0.2030.000
인구_내국인_인구수_여0.9300.9920.9970.9931.0000.992-0.179-0.278-0.286-0.2540.207
인구_내국인_전월말인구수0.9451.0000.9980.9950.9921.000-0.193-0.207-0.218-0.1830.000
인구_내국인_인구증감전월대비-0.187-0.191-0.193-0.199-0.179-0.1931.000-0.042-0.048-0.0310.000
외국인_계-0.046-0.206-0.243-0.227-0.278-0.207-0.0421.0000.9930.9930.000
외국인_남-0.062-0.217-0.253-0.237-0.286-0.218-0.0480.9931.0000.9790.000
외국인_여-0.027-0.183-0.220-0.203-0.254-0.183-0.0310.9930.9791.0000.171
구0.0000.0000.0000.0000.2070.0000.0000.0000.0000.1711.000

Missing values

2023-12-12T23:56:40.794350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:56:40.978681image/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

구구_동세대수인구_합계인구_내국인_인구수_계인구_내국인_인구수_남인구_내국인_인구수_여인구_내국인_전월말인구수인구_내국인_인구증감전월대비외국인_계외국인_남외국인_여데이터기준일자
0장안구파장동106862383323486118351165123801323471601872021-05-31
1장안구율천동203514507643156229002025645165-89192011617592021-05-31
2장안구정자1동110142956929388144991488929618-49181801012021-05-31
3장안구정자2동125533003029608145021510630095-654222012212021-05-31
4장안구정자3동143334238342203210852111842461-7818088922021-05-31
5장안구영화동10629217522001410240977421761-917389148242021-05-31
6장안구송죽동888019855196099737987219860-52461171292021-05-31
7장안구조원1동127672867428353143081404528728-543211291922021-05-31
8장안구조원2동657718937189049107979718959-22339242021-05-31
9장안구연무동849117097160868285780117096110114185932021-05-31
구구_동세대수인구_합계인구_내국인_인구수_계인구_내국인_인구수_남인구_내국인_인구수_여인구_내국인_전월말인구수인구_내국인_인구증감전월대비외국인_계외국인_남외국인_여데이터기준일자
34영통구매탄3동15684367733618018723174573676495933242692021-05-31
35영통구매탄4동94752211321820106681115222180-672931371562021-05-31
36영통구원천동18037398583888519688191973985539735044692021-05-31
37영통구영통1동126483584635352173741797835926-804942472472021-05-31
38영통구영통2동131032677826345139671237826843-654332252082021-05-31
39영통구영통3동133843431332814167451606934423-11014997687312021-05-31
40영통구망포1동1090831446312701576215508314311517679972021-05-31
41영통구망포2동94812815027995139761401928177-2715569862021-05-31
42영통구광교1동218725340452990259652702553420-164141742402021-05-31
43영통구광교2동1159829533293541417115183294854817986932021-05-31